Method and system for character recognition

ABSTRACT

Character recognition is described. In one embodiment, it may use matched sequences rather than character shape to determine a computer-legible result.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation-In-Part of the following, each ofwhich is hereby incorporated by reference in its entirety: U.S. patentapplication Ser. No. 11/004,637 filed on Dec. 3, 2004, U.S. patentapplication Ser. No. 11/097,961, filed Apr. 1, 2005, entitled METHODSAND SYSTEMS FOR INITIATING APPLICATION PROCESSES BY DATA CAPTURE FROMRENDERED DOCUMENTS; U.S. patent application Ser. No. 11/097,093, filedApr. 1, 2005, entitled DETERMINING ACTIONS INVOLVING CAPTUREDINFORMATION AND ELECTRONIC CONTENT ASSOCIATED WITH RENDERED DOCUMENTS,U.S. patent application Ser. No. 11/098,038, filed Apr. 1, 2005,entitled CONTENT ACCESS WITH HANDHELD DOCUMENT DATA CAPTURE DEVICES,U.S. patent application Ser. No. 11/098,014, filed Apr. 1, 2005,entitled SEARCH ENGINES AND SYSTEMS WITH HANDHELD DOCUMENT DATA CAPTUREDEVICES, U.S. patent application Ser. No. 11/097,103, filed Apr. 1,2005, entitled TRIGGERING ACTIONS IN RESPONSE TO OPTICALLY ORACOUSTICALLY CAPTURING KEYWORDS FROM A RENDERED DOCUMENT, U.S. patentapplication Ser. No. 11/098,043, filed Apr. 1, 2005, entitled SEARCHINGAND ACCESSING DOCUMENTS ON PRIVATE NETWORKS FOR USE WITH CAPTURES FROMRENDERED DOCUMENTS, U.S. patent application Ser. No. 11/097,981, filedApr. 1, 2005, entitled INFORMATION GATHERING SYSTEM AND METHOD, U.S.patent application Ser. No. 11/097,089, filed Apr. 1, 2005, entitledDOCUMENT ENHANCEMENT SYSTEM AND METHOD, U.S. patent application Ser. No.11/097,835, filed Apr. 1, 2005, entitled PUBLISHING TECHNIQUES FORADDING VALUE TO A RENDERED DOCUMENT, U.S. patent application Ser. No.11/098,016, filed Apr. 1, 2005, entitled ARCHIVE OF TEXT CAPTURES FROMRENDERED DOCUMENTS, U.S. patent application Ser. No. 11/097,828, filedApr. 1, 2005, entitled ADDING INFORMATION OR FUNCTIONALITY TO A RENDEREDDOCUMENT VIA ASSOCIATION WITH AN ELECTRONIC COUNTERPART, No. 11/097,833,filed Apr. 1, 2005, entitled AGGREGATE ANALYSIS OF TEXT CAPTURESPERFORMED BY MULTIPLE USERS FROM RENDERED DOCUMENTS, U.S. patentapplication Ser. No. 11/097,836, filed Apr. 1, 2005, entitledESTABLISHING AN INTERACTIVE ENVIRONMENT FOR RENDERED DOCUMENTS, U.S.patent application Ser. No. 11/098,042, filed Apr. 1, 2005, entitledDATA CAPTURE FROM RENDERED DOCUMENTS USING HANDHELD DEVICE, U.S. patentapplication Ser. No. 11/096,704, filed Apr. 1, 2005, entitled CAPTURINGTEXT FROM RENDERED DOCUMENTS USING SUPPLEMENTAL INFORMATION, U.S. patentapplication Ser. No. 11/110,353, filed Apr. 19, 2005, entitledPROCESSING TECHNIQUES FOR VISUAL CAPTURE DATA FROM A RENDERED DOCUMENT,U.S. patent application Ser. No. 11/131,945, filed May 17, 2005,entitled PROCESSING TECHNIQUES FOR TEXT CAPTURE FROM A RENDEREDDOCUMENT, U.S. patent application Ser. No. 11/185,908, filed Jul. 19,2005, entitled AUTOMATIC MODIFICATION OF WEB PAGES, U.S. patentapplication Ser. No. ______, filed Aug. 18, 2005, entitled SCANNERHAVING CONNECTED AND UNCONNECTED OPERATIONAL BEHAVIORS (attorney docketno. 43518.8081.US01), U.S. patent application Ser. No. ______, filedAug. 18, 2005, entitled LOCATING ELECTRONIC INSTANCES OF DOCUMENTS BASEDON RENDERED INSTANCES, DOCUMENT FRAGMENT DIGEST GENERATION, AND DIGESTBASED DOCUMENT FRAGMENT DETERMINATION (attorney docket no.43518.8082.US01), U.S. patent application Ser. No. ______, filed Aug.18, 2005, entitled METHODS, SYSTEMS AND COMPUTER PROGRAM PRODUCTS FORDATA GATHERING IN A DIGITAL AND HARD COPY DOCUMENT ENVIRONMENT,(attorney docket no. 43518.8085.US01), U.S. patent application Ser. No.______, filed Aug. 18, 2005, entitled APPLYING SCANNED INFORMATION TOIDENTIFY CONTENT (attorney docket no. 43518.8098.US01).

This application claims priority to, and incorporates by reference intheir entirety, the following U.S. Provisional Patent Applications:Application No. 60/604,103 filed on Aug. 23, 2004, Application No.60/604,098 filed on Aug. 23, 2004, Application No. 60/604,100 filed onAug. 23, 2004, Application No. 60/604,102 filed on Aug. 23, 2004,Application No. 60/605,229 filed on Aug. 27, 2004, Application No.60/605,105 filed on Aug. 27, 2004, Application No. 60/613,243 filed onSep. 27, 2004, Application No. 60/613,628 filed on Sep. 27, 2004,Application No. 60/613,632 filed on Sep. 27, 2004, Application No.60/613,589 filed on Sep. 27, 2004, Application No. 60/613,242 filed onSep. 27, 2004, Application No. 60/613,602 filed on Sep. 27, 2004,Application No. 60/613,340 filed on Sep. 27, 2004, Application No.60/613,634 filed on Sep. 27, 2004, Application No. 60/613,461 filed onSep. 27, 2004, Application No. 60/613,455 filed on Sep. 27, 2004,Application No. 60/613,460 filed on Sep. 27, 2004, Application No.60/613,400 filed on Sep. 27, 2004, Application No. 60/613,456 filed onSep. 27, 2004, Application No. 60/613,341 filed on Sep. 27, 2004,Application No. 60/613,361 filed on Sep. 27, 2004, Application No.60/613,454 filed on Sep. 27, 2004, Application No. 60/613,339 filed onSep. 27, 2004, Application No. 60/613,633 filed on Sep. 27, 2004,Application No. 60/615,378 filed on Oct. 1, 2004, Application No.60/615,112 filed on Oct. 1, 2004, Application No. 60/615,538 filed onOct. 1, 2004, Application No. 60/617,122 filed on Oct. 7, 2004,Application No. 60/622,906 filed on Oct. 28, 2004, Application No.60/633,452 filed on Dec. 6, 2004, Application No. 60/633,678 filed onDec. 6, 2004, Application No. 60/633,486 filed on Dec. 6, 2004,Application No. 60/633,453 filed on Dec. 6, 2004, Application No.60/634,627 filed on Dec. 9, 2004, Application No. 60/634,739 filed onDec. 9, 2004, Application No. 60/647,684 filed on Jan. 26, 2005,Application No. 60/648,746 filed on Jan. 31, 2005, Application No.60/653,372 filed on Feb. 15, 2005, Application No. 60/653,663 filed onFeb. 16, 2005, Application No. 60/653,669 filed on Feb. 16, 2005,Application No. 60/653,899 filed on Feb. 16, 2005, Application No.60/653,679 filed on Feb. 16, 2005, Application No. 60/653,847 filed onFeb. 16, 2005, Application No. 60/654,379 filed on Feb. 17, 2005,Application No. 60/654,368 filed on Feb. 18, 2005, Application No.60/654,326 filed on Feb. 18, 2005, Application No. 60/654,196 filed onFeb. 18, 2005, Application No. 60/655,279 filed on Feb. 22, 2005,Application No. 60/655,280 filed on Feb. 22, 2005, Application No.60/655,987 filed on Feb. 22, 2005, Application No. 60/655,697 filed onFeb. 22, 2005, Application No. 60/655,281 filed on Feb. 22, 2005, andApplication No. 60/657,309 filed on Feb. 28, 2005.

This application incorporates by reference in their entirety, thefollowing U.S. Provisional Patent Applications: Application No.60/563,520 filed on Apr. 19, 2004, Application No. 60/563,485 filed onApr. 19, 2004, Application No. 60/564,688 filed on Apr. 23, 2004,Application No. 60/564,846 filed on Apr. 23, 2004, Application No.60/566,667 filed on Apr. 30, 2004, Application No. 60/571,381 filed onMay 14, 2004, Application No. 60/571,560 filed on May 14, 2004,Application No. 60/571,715 filed on May 17, 2004, Application No.60/589,203 filed on Jul. 19, 2004, Application No. 60/589,201 filed onJul. 19, 2004, Application No. 60/589,202 filed on Jul. 19, 2004,Application No. 60/598,821 filed on Aug. 2, 2004, Application No.60/602,956 filed on Aug. 18, 2004, Application No. 60/602,925 filed onAug. 18, 2004, Application No. 60/602,947 filed on Aug. 18, 2004,Application No. 60/602,897 filed on Aug. 18, 2004, Application No.60/602,896 filed on Aug. 18, 2004, Application No. 60/602,930 filed onAug. 18, 2004, Application No. 60/602,898 filed on Aug. 18, 2004,Application No. 60/603,466 filed on Aug. 19, 2004, Application No.60/603,082 filed on Aug. 19, 2004, Application No. 60/603,081 filed onAug. 19, 2004, Application No. 60/603,498 filed on Aug. 20, 2004,Application No. 60/603,358 filed on Aug. 20, 2004.

TECHNICAL FIELD

The present disclosure relates generally to creating a computer-legibleform of printed content, and more particularly to optical characterrecognition.

BACKGROUND

A lot of the work that we do today is about information. We do researchto gather information, we experiment to create information, and wecommunicate to share this information. As such, information comes inmany forms. Unfortunately, these forms of information are not alwaysaccessible to each other. Information on paper is often seen asportable, and easy to interface with. Computers, on the other hand, areable to store much larger amounts of information and to search thisfaster than a person with paper, in many cases.

Many computers cannot access the vast quantities of printed information,however. One solution to is have a paperless workplace. The idea is thatall information is electronic, to which computers will always haveaccess. Paper is still a very useful way of storing and communicatingdata for people, though, and as such it thrives in modern workplaces.Another approach is Optical Character Recognition (OCR), in whichcomputers are programmed to read from a paper document.

U.S. Pat. No. 5,684,891 exemplifies this approach. OCR systems oftenimage or scan a printed document to create an electronic image of thedocument. Many systems start by separating the image into meaningfulparts, such as text, words, or characters. Some systems, such as U.S.Pat. No. 5,212,739, are able to process a word using specializedtechniques (e.g. micro-features of the represented characters). Most OCRsystems, however, examine each character individually. Techniques suchas matrix matching and feature extraction are used to create arepresentation of the character from the image. These techniquesgenerally attempt to determine which pixels are unnecessary, or grouprepresentative pixels with concepts such as a vector, or center of mass.Often, these representations are compared to known templates, such as inmatrix matching. In this way, a computer is effectively able to ask“Does this look more like an ‘a’ or a ‘b’ ?” Sometimes, theserepresentations are characterized to determine a most likely match, e.g.“Which character is thin with a dot on top?”

Recently, some techniques have employed substitution ciphers, such asU.S. Pat. No. 6,658,151. These methods are often used for documentmatching or language identification. This may be because these resultsare probabilistic estimates and not definitive results. Such a techniquemay associate each identifier with a character one at a time.

Some of these approaches often require a large amount of imageprocessing, a compute intensive task. Some of these approaches are oftenunable to identify characters in a font that is very different fromthose that this system has been programmed to interpret. Many of theseapproaches are unable to learn about a document as they scan it, e.g. ifa scanner doesn't recognize an ‘e’ the first time, it will not be ableto recognize any ‘e’.

Thus, there is a need for an approach to character recognition that ismore computationally efficient, and is able to make use of informationbeyond pre-programmed representations of characters. There is also aneed for character recognition that may determine multiple characters ata time or to more accurately associate identifiers with characters.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a data flow diagram that illustrates the flow of informationin one embodiment of the core system.

FIG. 2 is a component diagram of components included in a typicalimplementation of the system in the context of a typical operatingenvironment.

FIG. 3 is a block diagram of an embodiment of a scanner.

FIG. 4 is a block diagram of an embodiment of the invention.

FIG. 5 is a flow diagram of an embodiment of the invention.

FIG. 6 is a flow diagram of one embodiment of one method of determininga matched sequence of unrecognized images.

FIG. 7 is a conceptual illustration of one embodiment of one method ofdetermining a matched sequence of unrecognized images.

FIG. 8 is a flow diagram of one embodiment of one method of determininga matched sequence of unrecognized images.

FIG. 9 is a conceptual illustration of one embodiment of one method ofdetermining a matched sequence of unrecognized images.

FIG. 10 is a conceptual illustration of one embodiment of one method ofdetermining a matched sequence of unrecognized images.

FIG. 11 is a flow diagram of one embodiment of a step in one embodimentof one method of determining a matched sequence of unrecognized images.

FIG. 12 is a flow diagram showing steps typically performed by thesystem to determine a computer-legible result.

DETAILED DESCRIPTION

Overview

A device for interacting with rendered documents includes a scanner andmemory. The device can include a small, stand-alone scanning device, orit may include at least part of another device, such as at least part ofa pen-style device, at least part of a mouse, at least part of a cellphone, at least part of a remote control, or at least part of a personaldigital assistant, among numerous possibilities. The device can includeone or more means to establish communication with at least one of aseparate computing device or a network, and can be used both when suchcommunication is currently established (“on-line”) and also in theabsence of any such established communication (“off-line”).

Information from rendered documents is scanned to the memory, and a userof the device is signaled when sufficient information has been obtainedto determine at least one action associated with the scannedinformation. For example, the user may be signaled when sufficientinformation has been obtained to determine at least one documentcomprising the scanned information, or the user may be signaled whenenough information has been has been obtained to determine a commerceoperation, an annotation, a format operation, or identifying anadvertisement.

Determining when sufficient information has been obtained may involveascertaining a type or types of the scanned symbols, counts or relativepositions of the scanned symbols of various types, an overall number ofscanned symbols, a number of scanned words, a count of stop and non-stopwords or symbols, or the frequency of occurrence of words or symbols ofvarious types, among other things. The methods used for making such adetermination may differ depending on whether the device is on-line oroff-line.

Scan context information may be stored in association with the scannedinformation in the memory. The scan context information may facilitatedetermination of an action or actions using or otherwise involving thescanned information.

Subsequent to the scanning, upon establishing communication between thedevice and at least one of a separate computing device or a network, thescanned information may be transferred to at least one of the separatecomputing device or the network, for further processing and to carry outactions using or involving the scanned information. Instances of scannedinformation may be accumulated in a repository for the user, and may bedistinguished according to numerous criteria, including, for example,whether the instance was collected on-line or off-line, whether thesystem has presented the user with one or more possible actions that maybe associated with the instance, and whether the user has indicated oneor more actions to be performed with respect to the instance.

Part I—Introduction

1. Nature of the System

For every paper document that has an electronic counterpart, thereexists a discrete amount of information in the paper document that canidentify the electronic counterpart. In some embodiments, the systemuses a sample of text captured from a paper document, for example usinga handheld scanner, to identify and locate an electronic counterpart ofthe document. In most cases, the amount of text needed by the facilityis very small in that a few words of text from a document can oftenfunction as an identifier for the paper document and as a link to itselectronic counterpart. In addition, the system may use those few wordsto identify not only the document, but also a location within thedocument.

Thus, paper documents and their digital counterparts can be associatedin many useful ways using the system discussed herein.

1.1. A Quick Overview of the Future

Once the system has associated a piece of text in a paper document witha particular digital entity has been established, the system is able tobuild a huge amount of functionality on that association.

It is increasingly the case that most paper documents have an electroniccounterpart that is accessible on the World Wide Web or from some otheronline database or document corpus, or can be made accessible, such asin response to the payment of a fee or subscription. At the simplestlevel, then, when a user scans a few words in a paper document, thesystem can retrieve that electronic document or some part of it, ordisplay it, email it to somebody, purchase it, print it or post it to aweb page. As additional examples, scanning a few words of a book that aperson is reading over breakfast could cause the audio-book version inthe person's car to begin reading from that point when s/he startsdriving to work, or scanning the serial number on a printer cartridgecould begin the process of ordering a replacement.

The system implements these and many other examples of “paper/digitalintegration” without requiring changes to the current processes ofwriting, printing and publishing documents, giving such conventionalrendered documents a whole new layer of digital functionality.

1.2. Terminology

A typical use of the system begins with using an optical scanner to scantext from a paper document, but it is important to note that othermethods of capture from other types of document are equally applicable.The system is therefore sometimes described as scanning or capturingtext from a rendered document, where those terms are defined as follows:

A rendered document is a printed document or a document shown on adisplay or monitor. It is a document that is perceptible to a human,whether in permanent form or on a transitory display.

Scanning or capturing is the process of systematic examination to obtaininformation from a rendered document. The process may involve opticalcapture using a scanner or camera (for example a camera in a cellphone),or it may involve reading aloud from the document into an audio capturedevice or typing it on a keypad or keyboard. For more examples, seeSection 15.

2. Introduction to the System

This section describes some of the devices, processes and systems thatconstitute a system for paper/digital integration. In variousembodiments, the system builds a wide variety of services andapplications on this underlying core that provides the basicfunctionality.

2.1. The Processes

FIG. 1 is a data flow diagram that illustrates the flow of informationin one embodiment of the core system. Other embodiments may not use allof the stages or elements illustrated here, while some will use manymore.

Text from a rendered document is captured 100, typically in optical formby an optical scanner or audio form by a voice recorder, and this imageor sound data is then processed 102, for example to remove artifacts ofthe capture process or to improve the signal-to-noise ratio. Arecognition process 104 such as OCR, speech recognition, orautocorrelation then converts the data into a signature, comprised insome embodiments of text, text offsets, or other symbols. Alternatively,the system performs an alternate form of extracting document signaturefrom the rendered document. The signature represents a set of possibletext transcriptions in some embodiments. This process may be influencedby feedback from other stages, for example, if the search process andcontext analysis 110 have identified some candidate documents from whichthe capture may originate, thus narrowing the possible interpretationsof the original capture.

A post-processing 106 stage may take the output of the recognitionprocess and filter it or perform such other operations upon it as may beuseful. Depending upon the embodiment implemented, it may be possible atthis stage to deduce some direct actions 107 to be taken immediatelywithout reference to the later stages, such as where a phrase or symbolhas been captured which contains sufficient information in itself toconvey the user's intent. In these cases no digital counterpart documentneed be referenced, or even known to the system.

Typically, however, the next stage will be to construct a query 108 or aset of queries for use in searching. Some aspects of the queryconstruction may depend on the search process used and so cannot beperformed until the next stage, but there will typically be someoperations, such as the removal of obviously misrecognized or irrelevantcharacters, which can be performed in advance.

The query or queries are then passed to the search and context analysisstage 110. Here, the system optionally attempts to identify the documentfrom which the original data was captured. To do so, the systemtypically uses search indices and search engines 112, knowledge aboutthe user 114 and knowledge about the user's context or the context inwhich the capture occurred 116. Search engine 112 may employ and/orindex information specifically about rendered documents, about theirdigital counterpart documents, and about documents that have a web(internet) presence). It may write to, as well as read from, many ofthese sources and, as has been mentioned, it may feed information intoother stages of the process, for example by giving the recognitionsystem 104 information about the language, font, rendering and likelynext words based on its knowledge of the candidate documents.

In some circumstances the next stage will be to retrieve 120 a copy ofthe document or documents that have been identified. The sources of thedocuments 124 may be directly accessible, for example from a localfiling system or database or a web server, or they may need to becontacted via some access service 122 which might enforceauthentication, security or payment or may provide other services suchas conversion of the document into a desired format.

Applications of the system may take advantage of the association ofextra functionality or data with part or all of a document. For example,advertising applications discussed in Section 10.4 may use anassociation of particular advertising messages or subjects with portionsof a document. This extra associated functionality or data can bethought of as one or more overlays on the document, and is referred toherein as “markup.” The next stage of the process 130, then, is toidentify any markup relevant to the captured data. Such markup may beprovided by the user, the originator, or publisher of the document, orsome other party, and may be directly accessible from some source 132 ormay be generated by some service 134. In various embodiments, markup canbe associated with, and apply to, a rendered document and/or the digitalcounterpart to a rendered document, or to groups of either or both ofthese documents.

Lastly, as a result of the earlier stages, some actions may be taken140. These may be default actions such as simply recording theinformation found, they may be dependent on the data or document, orthey may be derived from the markup analysis. Sometimes the action willsimply be to pass the data to another system. In some cases the variouspossible actions appropriate to a capture at a specific point in arendered document will be presented to the user as a menu on anassociated display, for example on a local display 332, on a computerdisplay 212 or a mobile phone or PDA display 216. If the user doesn'trespond to the menu, the default actions can be taken.

2.2. The Components

FIG. 2 is a component diagram of components included in a typicalimplementation of the system in the context of a typical operatingenvironment. As illustrated, the operating environment includes one ormore optical scanning capture devices 202 or voice capture devices 204.In some embodiments, the same device performs both functions. Eachcapture device is able to communicate with other parts of the systemsuch as a computer 212 and a mobile station 216 (e.g., a mobile phone orPDA) using either a direct wired or wireless connection, or through thenetwork 220, with which it can communicate using a wired or wirelessconnection, the latter typically involving a wireless base station 214.In some embodiments, the capture device is integrated in the mobilestation, and optionally shares some of the audio and/or opticalcomponents used in the device for voice communications andpicture-taking.

Computer 212 may include a memory containing computer executableinstructions for processing an order from scanning devices 202 and 204.As an example, an order can include an identifier (such as a serialnumber of the scanning device 202/204 or an identifier that partially oruniquely identifies the user of the scanner), scanning contextinformation (e.g., time of scan, location of scan, etc.) and/or scannedinformation (such as a text string) that is used to uniquely identifythe document being scanned. In alternative embodiments, the operatingenvironment may include more or less components.

Also available on the network 220 are search engines 232, documentsources 234, user account services 236, markup services 238 and othernetwork services 239. The network 220 may be a corporate intranet, thepublic Internet, a mobile phone network or some other network, or anyinterconnection of the above.

Regardless of the manner by which the devices are coupled to each other,they may all may be operable in accordance with well-known commercialtransaction and communication protocols (e.g., Internet Protocol (IP)).In various embodiments, the functions and capabilities of scanningdevice 202, computer 212, and mobile station 216 may be wholly orpartially integrated into one device. Thus, the terms scanning device,computer, and mobile station can refer to the same device depending uponwhether the device incorporates functions or capabilities of thescanning device 202, computer 212 and mobile station 216. In addition,some or all of the functions of the search engines 232, document sources234, user account services 236, markup services 238 and other networkservices 239 may be implemented on any of the devices and/or otherdevices not shown.

2.3. The Capture Device

As described above, the capture device may capture text using an opticalscanner that captures image data from the rendered document, or using anaudio recording device that captures a user's spoken reading of thetext, or other methods. Some embodiments of the capture device may alsocapture images, graphical symbols and icons, etc., including machinereadable codes such as barcodes. The device may be exceedingly simple,consisting of little more than the transducer, some storage, and a datainterface, relying on other functionality residing elsewhere in thesystem, or it may be a more full-featured device. For illustration, thissection describes a device based around an optical scanner and with areasonable number of features.

Scanners are well known devices that capture and digitize images. Anoffshoot of the photocopier industry, the first scanners were relativelylarge devices that captured an entire document page at once. Recently,portable optical scanners have been introduced in convenient formfactors, such as a pen-shaped handheld device.

In some embodiments, the portable scanner is used to scan text,graphics, or symbols from rendered documents. The portable scanner has ascanning element that captures text, symbols, graphics, etc, fromrendered documents. In addition to documents that have been printed onpaper, in some embodiments, rendered documents include documents thathave been displayed on a screen such as a CRT monitor or LCD display.

FIG. 3 is a block diagram of an embodiment of a scanner 302. The scanner302 comprises an optical scanning head 308 to scan information fromrendered documents and convert it to machine-compatible data, and anoptical path 306, typically a lens, an aperture or an image conduit toconvey the image from the rendered document to the scanning head. Thescanning head 308 may incorporate a Charge-Coupled Device (CCD), aComplementary Metal Oxide Semiconductor (CMOS) imaging device, or anoptical sensor of another type.

A microphone 310 and associated circuitry convert the sound of theenvironment (including spoken words) into machine-compatible signals,and other input facilities exist in the form of buttons, scroll-wheelsor other tactile sensors such as touch-pads 314.

Feedback to the user is possible through a visual display or indicatorlights 332, through a loudspeaker or other audio transducer 334 andthrough a vibrate module 336.

The scanner 302 comprises logic 326 to interact with the various othercomponents, possibly processing the received signals into differentformats and/or interpretations. Logic 326 may be operable to read andwrite data and program instructions stored in associated storage 330such as RAM, ROM, flash, or other suitable memory. It may read a timesignal from the clock unit 328. The scanner 302 also includes aninterface 316 to communicate scanned information and other signals to anetwork and/or an associated computing device. In some embodiments, thescanner 302 may have an on-board power supply 332. In other embodiments,the scanner 302 may be powered from a tethered connection to anotherdevice, such as a Universal Serial Bus (USB) connection.

As an example of one use of scanner 302, a reader may scan some textfrom a newspaper article with scanner 302. The text is scanned as abit-mapped image via the scanning head 308. Logic 326 causes thebit-mapped image to be stored in memory 330 with an associatedtime-stamp read from the clock unit 328. Logic 326 may also performoptical character recognition (OCR) or other post-scan processing on thebit-mapped image to convert it to text. Logic 326 may optionally extracta signature from the image, for example by performing a convolution-likeprocess to locate repeating occurrences of characters, symbols orobjects, and determine the distance or number of other characters,symbols, or objects between these repeated elements. The reader may thenupload the bit-mapped image (or text or other signature, if post-scanprocessing has been performed by logic 326) to an associated computervia interface 316.

As an example of another use of scanner 302, a reader may capture sometext from an article as an audio file by using microphone 310 as anacoustic capture port. Logic 326 causes audio file to be stored inmemory 328. Logic 326 may also perform voice recognition or otherpost-scan processing on the audio file to convert it to text. As above,the reader may then upload the audio file (or text produced by post-scanprocessing performed by logic 326) to an associated computer viainterface 316.

Part II—Overview of the Areas of the Core System

As paper-digital integration becomes more common, there are many aspectsof existing technologies that can be changed to take better advantage ofthis integration, or to enable it to be implemented more effectively.This section highlights some of those issues.

3. Search

Searching a corpus of documents, even so large a corpus as the WorldWide Web, has become commonplace for ordinary users, who use a keyboardto construct a search query which is sent to a search engine. Thissection and the next discuss the aspects of both the construction of aquery originated by a capture from a rendered document, and the searchengine that handles such a query.

3.1. Scan/Speak/Type as Search Query

Use of the described system typically starts with a few words beingcaptured from a rendered document using any of several methods,including those mentioned in Section 1.2 above. Where the input needssome interpretation to convert it to text, for example in the case ofOCR or speech input, there may be end-to-end feedback in the system sothat the document corpus can be used to enhance the recognition process.End-to-end feedback can be applied by performing an approximation of therecognition or interpretation; identifying a set of one or morecandidate matching documents, and then using information from thepossible matches in the candidate documents to further refine orrestrict the recognition or interpretation. Candidate documents can beweighted according to their probable relevance (for example, based onthen number of other users who have scanned in these documents, or theirpopularity on the Internet), and these weights can be applied in thisiterative recognition process.

3.2. Short Phrase Searching

Because the selective power of a search query based on a few words isgreatly enhanced when the relative positions of these words are known,only a small amount of text need be captured for the system to identifythe text's location in a corpus. Most commonly, the input text will be acontiguous sequence of words, such as a short phrase.

3.2.1. Finding Document and Location in Document from Short Capture

In addition to locating the document from which a phrase originates, thesystem can identify the location in that document and can take actionbased on this knowledge.

3.2.2. Other Methods of Finding Location

The system may also employ other methods of discovering the document andlocation, such as by using watermarks or other special markings on therendered document.

3.3. Incorporation of Other Factors in Search Query

In addition to the captured text, other factors (i.e., information aboutuser identity, profile, and context) may form part of the search query,such as the time of the capture, the identity and geographical locationof the user, knowledge of the user's habits and recent activities, etc.

The document identity and other information related to previouscaptures, especially if they were quite recent, may form part of asearch query.

The identity of the user may be determined from a unique identifierassociated with a capturing device, and/or biometric or othersupplemental information (speech patterns, fingerprints, etc.).

3.4. Knowledge of Nature of Unreliability in Search Query (OCR ErrorsEtc)

The search query can be constructed taking into account the types oferrors likely to occur in the particular capture method used. Oneexample of this is an indication of suspected errors in the recognitionof specific characters; in this instance a search engine may treat thesecharacters as wildcards, or assign them a lower priority.

3.5. Local Caching of Index for Performance/Offline Use

Sometimes the capturing device may not be in communication with thesearch engine or corpus at the time of the data capture. For thisreason, information helpful to the offline use of the device may bedownloaded to the device in advance, or to some entity with which thedevice can communicate. In some cases, all or a substantial part of anindex associated with a corpus may be downloaded. This topic isdiscussed further in Section 15.3.

3.6. Queries, in Whatever Form, May be Recorded and Acted on Later

If there are likely to be delays or cost associated with communicating aquery or receiving the results, this pre-loaded information can improvethe performance of the local device, reduce communication costs, andprovide helpful and timely user feedback.

In the situation where no communication is available (the local deviceis “offline”), the queries may be saved and transmitted to the rest ofthe system at such a time as communication is restored.

In these cases it may be important to transmit a timestamp with eachquery. The time of the capture can be a significant factor in theinterpretation of the query. For example, Section 13.1 discusses theimportance of the time of capture in relation to earlier captures. It isimportant to note that the time of capture will not always be the sameas the time that the query is executed.

3.7. Parallel Searching

For performance reasons, multiple queries may be launched in response toa single capture, either in sequence or in parallel. Several queries maybe sent in response to a single capture, for example as new words areadded to the capture, or to query multiple search engines in parallel.

For example, in some embodiments, the system sends queries to a specialindex for the current document, to a search engine on a local machine,to a search engine on the corporate network, and to remote searchengines on the Internet.

The results of particular searches may be given higher priority thanthose from others.

The response to a given query may indicate that other pending queriesare superfluous; these may be cancelled before completion.

4. Paper and Search Engines

Often it is desirable for a search engine that handles traditionalonline queries also to handle those originating from rendered documents.Conventional search engines may be enhanced or modified in a number ofways to make them more suitable for use with the described system.

The search engine and/or other components of the system may create andmaintain indices that have different or extra features. The system maymodify an incoming paper-originated query or change the way the query ishandled in the resulting search, thus distinguishing thesepaper-originated queries from those coming from queries typed into webbrowsers and other sources. And the system may take different actions oroffer different options when the results are returned by the searchesoriginated from paper as compared to those from other sources. Each ofthese approaches is discussed below.

4.1. Indexing

Often, the same index can be searched using either paper-originated ortraditional queries, but the index may be enhanced for use in thecurrent system in a variety of ways.

4.1.1. Knowledge about the Paper Form

Extra fields can be added to such an index that will help in the case ofa paper-based search.

Index Entry Indicating Document Availability in Paper Form

The first example is a field indicating that the document is known toexist or be distributed in paper form. The system may give suchdocuments higher priority if the query comes from paper.

Knowledge of Popularity Paper Form

In this example statistical data concerning the popularity of paperdocuments (and, optionally, concerning sub-regions within thesedocuments)—for example the amount of scanning activity, circulationnumbers provided by the publisher or other sources, etc—is used to givesuch documents higher priority, to boost the priority of digitalcounterpart documents (for example, for browser-based queries or websearches), etc.

Knowledge of Rendered Format

Another important example may be recording information about the layoutof a specific rendering of a document.

For a particular edition of a book, for example, the index may includeinformation about where the line breaks and page breaks occur, whichfonts were used, any unusual capitalization.

The index may also include information about the proximity of otheritems on the page, such as images, text boxes, tables andadvertisements.

Use of Semantic Information in Original

Lastly, semantic information that can be deduced from the source markupbut is not apparent in the paper document, such as the fact that aparticular piece of text refers to an item offered for sale, or that acertain paragraph contains program code, may also be recorded in theindex.

4.1.2. Indexing in the Knowledge of the Capture Method

A second factor that may modify the nature of the index is the knowledgeof the type of capture likely to be used. A search initiated by anoptical scan may benefit if the index takes into account characters thatare easily confused in the OCR process, or includes some knowledge ofthe fonts used in the document. Similarly, if the query is from speechrecognition, an index based on similar-sounding phonemes may be muchmore efficiently searched. An additional factor that may affect the useof the index in the described model is the importance of iterativefeedback during the recognition process. If the search engine is able toprovide feedback from the index as the text is being captured, it cangreatly increase the accuracy of the capture.

Indexing Using Offsets

If the index is likely to be searched using theoffset-based/autocorrelation OCR methods described in Section 9, in someembodiments, the system stores the appropriate offset or signatureinformation in an index.

4.1.3. Multiple Indices

Lastly, in the described system, it may be common to conduct searches onmany indices. Indices may be maintained on several machines on acorporate network. Partial indices may be downloaded to the capturedevice, or to a machine close to the capture device. Separate indicesmay be created for users or groups of users with particular interests,habits or permissions. An index may exist for each filesystem, eachdirectory, even each file on a user's hard disk. Indexes are publishedand subscribed to by users and by systems. It will be important, then,to construct indices that can be distributed, updated, merged andseparated efficiently.

4.2. Handling the Queries

4.2.1. Knowing the Capture is from Paper

A search engine may take different actions when it recognizes that asearch query originated from a paper document. The engine might handlethe query in a way that is more tolerant to the types of errors likelyto appear in certain capture methods, for example.

It may be able to deduce this from some indicator included in the query(for example a flag indicating the nature of the capture), or it maydeduce this from the query itself (for example, it may recognize errorsor uncertainties typical of the OCR process).

Alternatively, queries from a capture device can reach the engine by adifferent channel or port or type of connection than those from othersources, and can be distinguished in that way. For example, someembodiments of the system will route queries to the search engine by wayof a dedicated gateway. Thus, the search engine knows that all queriespassing through the dedicated gateway were originated from a paperdocument.

4.2.2. Use of Context

Section 13 below describes a variety of different factors which areexternal to the captured text itself, yet which can be a significant aidin identifying a document. These include such things as the history ofrecent scans, the longer-term reading habits of a particular user, thegeographic location of a user and the user's recent use of particularelectronic documents. Such factors are referred to herein as “context.”

Some of the context may be handled by the search engine itself, and bereflected in the search results. For example, the search engine may keeptrack of a user's scanning history, and may also cross-reference thisscanning history to conventional keyboard-based queries. In such cases,the search engine maintains and uses more state information about eachindividual user than do most conventional search engines, and eachinteraction with a search engine may be considered to extend overseveral searches and a longer period of time than is typical today.

Some of the context may be transmitted to the search engine in thesearch query (Section 3.3), and may possibly be stored at the engine soas to play a part in future queries. Lastly, some of the context willbest be handled elsewhere, and so becomes a filter or secondary searchapplied to the results from the search engine.

Data-Stream Input to Search

An important input into the search process is the broader context of howthe community of users is interacting with the rendered version of thedocument—for example, which documents are most widely read and by whom.There are analogies with a web search returning the pages that are mostfrequently linked to, or those that are most frequently selected frompast search results. For further discussion of this topic, see Sections13.4 and 14.2.

4.2.3. Document Sub-Regions

The described system can emit and use not only information aboutdocuments as a whole, but also information about sub-regions ofdocuments, even down to individual words. Many existing search enginesconcentrate simply on locating a document or file that is relevant to aparticular query. Those that can work on a finer grain and identify alocation within a document will provide a significant benefit for thedescribed system.

4.3. Returning the Results

The search engine may use some of the further information it nowmaintains to affect the results returned.

The system may also return certain documents to which the user hasaccess only as a result of being in possession of the paper copy(Section 7.4).

The search engine may also offer new actions or options appropriate tothe described system, beyond simple retrieval of the text.

5. Markup, Annotations and Metadata

In addition to performing the capture-search-retrieve process, thedescribed system also associates extra functionality with a document,and in particular with specific locations or segments of text within adocument. This extra functionality is often, though not exclusively,associated with the rendered document by being associated with itselectronic counterpart. As an example, hyperlinks in a web page couldhave the same functionality when a printout of that web page is scanned.In some cases, the functionality is not defined in the electronicdocument, but is stored or generated elsewhere.

This layer of added functionality is referred to herein as “markup.”

5.1. Overlays, Static and Dynamic

One way to think of the markup is as an “overlay” on the document, whichprovides further information about—and may specify actions associatedwith—the document or some portion of it. The markup may includehuman-readable content, but is often invisible to a user and/or intendedfor machine use. Examples include options to be displayed in apopup-menu on a nearby display when a user captures text from aparticular area in a rendered document, or audio samples that illustratethe pronunciation of a particular phrase.

5.1.1. Several Layers, Possibly from Several Sources

Any document may have multiple overlays simultaneously, and these may besourced from a variety of locations. Markup data may be created orsupplied by the author of the document, or by the user, or by some otherparty.

Markup data may be attached to the electronic document or embedded init. It may be found in a conventional location (for example, in the sameplace as the document but with a different filename suffix). Markup datamay be included in the search results of the query that located theoriginal document, or may be found by a separate query to the same oranother search engine. Markup data may be found using the originalcaptured text and other capture information or contextual information,or it may be found using already-deduced information about the documentand location of the capture. Markup data may be found in a locationspecified in the document, even if the markup itself is not included inthe document.

The markup may be largely static and specific to the document, similarto the way links on a traditional html web page are often embedded asstatic data within the html document, but markup may also be dynamicallygenerated and/or applied to a large number of documents. An example ofdynamic markup is information attached to a document that includes theup-to-date share price of companies mentioned in that document. Anexample of broadly applied markup is translation information that isautomatically available on multiple documents or sections of documentsin a particular language.

5.1.2. Personal “Plug-In” Layers

Users may also install, or subscribe to particular sources of, markupdata, thus personalizing the system's response to particular captures.

5.2. Keywords and Phrases, Trademarks and Logos

Some elements in documents may have particular “markup” or functionalityassociated with them based on their own characteristics rather thantheir location in a particular document. Examples include special marksthat are printed in the document purely for the purpose of beingscanned, as well as logos and trademarks that can link the user tofurther information about the organization concerned. The same appliesto “keywords” or “key phrases” in the text. Organizations might registerparticular phrases with which they are associated, or with which theywould like to be associated, and attach certain markup to them thatwould be available wherever that phrase was scanned.

Any word, phrase, etc. may have associated markup. For example, thesystem may add certain items to a pop-up menu (e.g., a link to an onlinebookstore) whenever the user captures the word “book,” or the title of abook, or a topic related to books. In some embodiments, of the system,digital counterpart documents or indices are consulted to determinewhether a capture occurred near the word “book,” or the title of a book,or a topic related to books—and the system behavior is modified inaccordance with this proximity to keyword elements. In the precedingexample, note that markup enables data captured from non-commercial textor documents to trigger a commercial transaction.

5.3. User-Supplied Content

5.3.1. User Comments and Annotations, Including Multimedia

Annotations are another type of electronic information that may beassociated with a document. For example, a user can attach an audio fileof his/her thoughts about a particular document for later retrieval asvoice annotations. As another example of a multimedia annotation, a usermay attach photographs of places referred to in the document. The usergenerally supplies annotations for the document but the system canassociate annotations from other sources (for example, other users in awork group may share annotations).

5.3.2. Notes from Proof-Reading

An important example of user-sourced markup is the annotation of paperdocuments as part of a proofreading, editing or reviewing process.

5.4. Third-Party Content

As mentioned earlier, markup data may often be supplied by thirdparties, such as by other readers of the document. Online discussionsand reviews are a good example, as are community-managed informationrelating to particular works, volunteer-contributed translations andexplanations.

Another example of third-party markup is that provided by advertisers.

5.5. Dynamic Markup Based on Other Users' Data Streams

By analyzing the data captured from documents by several or all users ofthe system, markup can be generated based on the activities andinterests of a community. An example might be an online bookstore thatcreates markup or annotations that tell the user, in effect, “People whoenjoyed this book also enjoyed . . . . ” The markup may be lessanonymous, and may tell the user which of the people in his/her contactlist have also read this document recently. Other examples of datastreamanalysis are included in Section 14.

5.6. Markup Based on External Events and Data Sources

Markup will often be based on external events and data sources, such asinput from a corporate database, information from the public Internet;or statistics gathered by the local operating system.

Data sources may also be more local, and in particular may provideinformation about the user's context—his/her identity, location andactivities. For example, the system might communicate with the user'smobile phone and offer a markup layer that gives the user the option tosend a document to somebody that the user has recently spoken to on thephone.

6. Authentication, Personalization and Security

In many situations, the identity of the user will be known. Sometimesthis will be an “anonymous identity,” where the user is identified onlyby the serial number of the capture device, for example. Typically,however, it is expected that the system will have a much more detailedknowledge of the user, which can be used for personalizing the systemand to allow activities and transactions to be performed in the user'sname.

6.1. User History and “Life Library”

One of the simplest and yet most useful functions that the system canperform is to keep a record for a user of the text that s/he hascaptured and any further information related to that capture, includingthe details of any documents found, the location within that documentand any actions taken as a result.

This stored history is beneficial for both the user and the system.

6.1.1. For the User

The user can be presented with a “Life Library,” a record of everythings/he has read and captured. This may be simply for personal interest,but may be used, for example, in a library by an academic who isgathering material for the bibliography of his next paper.

In some circumstances, the user may wish to make the library public,such as by publishing it on the web in a similar manner to a weblog, sothat others may see what s/he is reading and finds of interest.

Lastly, in situations where the user captures some text and the systemcannot immediately act upon the capture (for example, because anelectronic version of the document is not yet available) the capture canbe stored in the library and can be processed later, eitherautomatically or in response to a user request. A user can alsosubscribe to new markup services and apply them to previously capturedscans.

6.1.2. For the System

A record of a user's past captures is also useful for the system. Manyaspects of the system operation can be enhanced by knowing the user'sreading habits and history. The simplest example is that any scan madeby a user is more likely to come from a document that the user hasscanned in the recent past, and in particular if the previous scan waswithin the last few minutes it is very likely to be from the samedocument. Similarly, it is more likely that a document is being read instart-to-finish order. Thus, for English documents, it is also morelikely that later scans will occur farther down in the document. Suchfactors can help the system establish the location of the capture incases of ambiguity, and can also reduce the amount of text that needs tobe captured.

6.2. Scanner as Payment, Identity and Authentication Device

Because the capture process generally begins with a device of some sort,typically an optical scanner or voice recorder, this device may be usedas a key that identifies the user and authorizes certain actions.

6.2.1. Associate Scanner with Phone or Other Account

The device may be embedded in a mobile phone or in some other wayassociated with a mobile phone account. For example, a scanner may beassociated with a mobile phone account by inserting a SIM cardassociated with the account into the scanner. Similarly, the device maybe embedded in a credit card or other payment card, or have the facilityfor such a card to be connected to it. The device may therefore be usedas a payment token, and financial transactions may be initiated by thecapture from the rendered document.

6.2.2. Using Scanner Input for Authentication

The scanner may also be associated with a particular user or accountthrough the process of scanning some token, symbol or text associatedwith that user or account. In addition, scanner may be used forbiometric identification, for example by scanning the fingerprint of theuser. In the case of an audio-based capture device, the system mayidentify the user by matching the voice pattern of the user or byrequiring the user to speak a certain password or phrase.

For example, where a user scans a quote from a book and is offered theoption to buy the book from an online retailer, the user can select thisoption, and is then prompted to scan his/her fingerprint to confirm thetransaction.

See also Sections 15.5 and 15.6.

6.2.3. Secure Scanning Device

When the capture device is used to identify and authenticate the user,and to initiate transactions on behalf of the user, it is important thatcommunications between the device and other parts of the system aresecure. It is also important to guard against such situations as anotherdevice impersonating a scanner, and so-called “man in the middle”attacks where communications between the device and other components areintercepted.

Techniques for providing such security are well understood in the art;in various embodiments, the hardware and software in the device andelsewhere in the system are configured to implement such techniques.

7. Publishing Models and Elements

An advantage of the described system is that there is no need to alterthe traditional processes of creating, printing or publishing documentsin order to gain many of the system's benefits. There are reasons,though, that the creators or publishers of a document—hereafter simplyreferred to as the “publishers”—may wish to create functionality tosupport the described system.

This section is primarily concerned with the published documentsthemselves. For information about other related commercial transactions,such as advertising, see Section 10 entitled “P-Commerce.”

7.1. Electronic Companions to Printed Documents

The system allows for printed documents to have an associated electronicpresence. Conventionally publishers often ship a CD-ROM with a book thatcontains further digital information, tutorial movies and othermultimedia data, sample code or documents, or further referencematerials. In addition, some publishers maintain web sites associatedwith particular publications which provide such materials, as well asinformation which may be updated after the time of publishing, such aserrata, further comments, updated reference materials, bibliographiesand further sources of relevant data, and translations into otherlanguages. Online forums allow readers to contribute their commentsabout the publication.

The described system allows such materials to be much more closely tiedto the rendered document than ever before, and allows the discovery ofand interaction with them to be much easier for the user. By capturing aportion of text from the document, the system can automatically connectthe user to digital materials associated with the document, and moreparticularly associated with that specific part of the document.Similarly, the user can be connected to online communities that discussthat section of the text, or to annotations and commentaries by otherreaders. In the past, such information would typically need to be foundby searching for a particular page number or chapter.

An example application of this is in the area of academic textbooks(Section 17.5).

7.2. “Subscriptions” to Printed Documents

Some publishers may have mailing lists to which readers can subscribe ifthey wish to be notified of new relevant matter or when a new edition ofthe book is published. With the described system, the user can registeran interest in particular documents or parts of documents more easily,in some cases even before the publisher has considered providing anysuch functionality. The reader's interest can be fed to the publisher,possibly affecting their decision about when and where to provideupdates, further information, new editions or even completely newpublications on topics that have proved to be of interest in existingbooks.

7.3. Printed Marks with Special Meaning or Containing Special Data

Many aspects of the system are enabled simply through the use of thetext already existing in a document. If the document is produced in theknowledge that it may be used in conjunction with the system, however,extra functionality can be added by printing extra information in theform of special marks, which may be used to identify the text or arequired action more closely, or otherwise enhance the document'sinteraction with the system. The simplest and most important example isan indication to the reader that the document is definitely accessiblethrough the system. A special icon might be used, for example, toindicate that this document has an online discussion forum associatedwith it.

Such symbols may be intended purely for the reader, or they may berecognized by the system when scanned and used to initiate some action.Sufficient data may be encoded in the symbol to identify more than justthe symbol: it may also store information, for example about thedocument, edition, and location of the symbol, which could be recognizedand read by the system.

7.4. Authorization Through Possession of the Paper Document

There are some situations where possession of or access to the printeddocument would entitle the user to certain privileges, for example, theaccess to an electronic copy of the document or to additional materials.With the described system, such privileges could be granted simply as aresult of the user capturing portions of text from the document, orscanning specially printed symbols. In cases where the system needed toensure that the user was in possession of the entire document, it mightprompt the user to scan particular items or phrases from particularpages, e.g. “the second line of page 46.”

7.5. Documents which Expire

If the printed document is a gateway to extra materials andfunctionality, access to such features can also be time-limited. Afterthe expiry date, a user may be required to pay a fee or obtain a newerversion of the document to access the features again. The paper documentwill, of course, still be usable, but will lose some of its enhancedelectronic functionality. This may be desirable, for example, becausethere is profit for the publisher in receiving fees for access toelectronic materials, or in requiring the user to purchase new editionsfrom time to time, or because there are disadvantages associated withoutdated versions of the printed document remaining in circulation.Coupons are an example of a type of commercial document that can have anexpiration date.

7.6. Popularity Analysis and Publishing Decisions

Section 10.5 discusses the use of the system's statistics to influencecompensation of authors and pricing of advertisements.

In some embodiments, the system deduces the popularity of a publicationfrom the activity in the electronic community associated with it as wellas from the use of the paper document. These factors may help publishersto make decisions about what they will publish in future. If a chapterin an existing book, for example, turns out to be exceedingly popular,it may be worth expanding into a separate publication.

8. Document Access Services

An important aspect of the described system is the ability to provide toa user who has access to a rendered copy of a document access to anelectronic version of that document. In some cases, a document is freelyavailable on a public network or a private network to which the user hasaccess. The system uses the captured text to identify, locate andretrieve the document, in some cases displaying it on the user's screenor depositing it in their email inbox.

In some cases, a document will be available in electronic form, but fora variety of reasons may not be accessible to the user. There may not besufficient connectivity to retrieve the document, the user may not beentitled to retrieve it, there may be a cost associated with gainingaccess to it, or the document may have been withdrawn and possiblyreplaced by a new version, to name just a few possibilities. The systemtypically provides feedback to the user about these situations.

As mentioned in Section 7.4, the degree or nature of the access grantedto a particular user may be different if it is known that the useralready has access to a printed copy of the document.

8.1. Authenticated Document Access

Access to the document may be restricted to specific users, or to thosemeeting particular criteria, or may only be available in certaincircumstances, for example when the user is connected to a securenetwork. Section 6 describes some of the ways in which the credentialsof a user and scanner may be established.

8.2. Document Purchase—Copyright-Owner Compensation

Documents that are not freely available to the general public may stillbe accessible on payment of a fee, often as compensation to thepublisher or copyright-holder. The system may implement paymentfacilities directly or may make use of other payment methods associatedwith the user, including those described in Section 6.2.

8.3. Document Escrow and Proactive Retrieval

Electronic documents are often transient; the digital source version ofa rendered document may be available now but inaccessible in future. Thesystem may retrieve and store the existing version on behalf of theuser, even if the user has not requested it, thus guaranteeing itsavailability should the user request it in future. This also makes itavailable for the system's use, for example for searching as part of theprocess of identifying future captures.

In the event that payment is required for access to the document, atrusted “document escrow” service can retrieve the document on behalf ofthe user, such as upon payment of a modest fee, with the assurance thatthe copyright holder will be fully compensated in future if the usershould ever request the document from the service.

Variations on this theme can be implemented if the document is notavailable in electronic form at the time of capture. The user canauthorize the service to submit a request for or make a payment for thedocument on his/her behalf if the electronic document should becomeavailable at a later date.

8.4. Association with Other Subscriptions and Accounts

Sometimes payment may be waived, reduced or satisfied based on theuser's existing association with another account or subscription.Subscribers to the printed version of a newspaper might automatically beentitled to retrieve the electronic version, for example.

In other cases, the association may not be quite so direct: a user maybe granted access based on an account established by their employer, orbased on their scanning of a printed copy owned by a friend who is asubscriber.

8.5. Replacing Photocopying with Scan-and-Print

The process of capturing text from a paper document, identifying anelectronic original, and printing that original, or some portion of thatoriginal associated with the capture, forms an alternative totraditional photocopying with many advantages:

the paper document need not be in the same location as the finalprintout, and in any case need not be there at the same time

the wear and damage caused to documents by the photocopying process,especially to old, fragile and valuable documents, can be avoided

the quality of the copy is typically be much higher

records may be kept about which documents or portions of documents arethe most frequently copied

payment may be made to the copyright owner as part of the process

unauthorized copying may be prohibited

8.6. Locating Valuable Originals from Photocopies

When documents are particularly valuable, as in the case of legalinstruments or documents that have historical or other particularsignificance, people may typically work from copies of those documents,often for many years, while the originals are kept in a safe location.

The described system could be coupled to a database which records thelocation of an original document, for example in an archiving warehouse,making it easy for somebody with access to a copy to locate the archivedoriginal paper document.

9. Text Recognition Technologies

Optical Character Recognition (OCR) technologies have traditionallyfocused on images that include a large amount of text, for example froma flat-bed scanner capturing a whole page. OCR technologies often needsubstantial training and correcting by the user to produce useful text.OCR technologies often require substantial processing power on themachine doing the OCR, and, while many systems use a dictionary, theyare generally expected to operate on an effectively infinite vocabulary.

All of the above traditional characteristics may be improved upon in thedescribed system.

While this section focuses on OCR, many of the issues discussed mapdirectly onto other recognition technologies, in particular speechrecognition. As mentioned in Section 3.1, the process of capturing frompaper may be achieved by a user reading the text aloud into a devicewhich captures audio. Those skilled in the art will appreciate thatprinciples discussed here with respect to images, fonts, and textfragments often also apply to audio samples, user speech models andphonemes.

9.1. Optimization for Appropriate Devices

A scanning device for use with the described system will often be small,portable, and low power. The scanning device may capture only a fewwords at a time, and in some implementations does not even capture awhole character at once, but rather a horizontal slice through the text,many such slices being stitched together to form a recognizable signalfrom which the text may be deduced. The scanning device may also havevery limited processing power or storage so, while in some embodimentsit may perform all of the OCR process itself, many embodiments willdepend on a connection to a more powerful device, possibly at a latertime, to convert the captured signals into text. Lastly, it may havevery limited facilities for user interaction, so may need to defer anyrequests for user input until later, or operate in a “best-guess” modeto a greater degree than is common now.

9.2. “Uncertain” OCR

The primary new characteristic of OCR within the described system is thefact that it will, in general, examine images of text which existselsewhere and which may be retrieved in digital form. An exacttranscription of the text is therefore not always required from the OCRengine. The OCR system may output a set or a matrix of possible matches,in some cases including probability weightings, which can still be usedto search for the digital original.

9.3. Iterative OCR—Guess, Disambiguate, Guess . . .

If the device performing the recognition is able to contact the documentindex at the time of processing, then the OCR process can be informed bythe contents of the document corpus as it progresses, potentiallyoffering substantially greater recognition accuracy.

Such a connection will also allow the device to inform the user whensufficient text has been captured to identify the digital source.

9.4. Using Knowledge of Likely Rendering

When the system has knowledge of aspects of the likely printed renderingof a document—such as the font typeface used in printing, or the layoutof the page, or which sections are in italics—this too can help in therecognition process. (Section 4.1.1)

9.5. Font Caching—Determine Font on Host, Download to Client

As candidate source texts in the document corpus are identified, thefont, or a rendering of it, may be downloaded to the device to help withthe recognition.

9.6. Autocorrelation and Character Offsets

While component characters of a text fragment may be the most recognizedway to represent a fragment of text that may be used as a documentsignature, other representations of the text may work sufficiently wellthat the actual text of a text fragment need not be used when attemptingto locate the text fragment in a digital document and/or database, orwhen disambiguating the representation of a text fragment into areadable form. Other representations of text fragments may providebenefits that actual text representations lack. For example, opticalcharacter recognition of text fragments is often prone to errors, unlikeother representations of captured text fragments that may be used tosearch for and/or recreate a text fragment without resorting to opticalcharacter recognition for the entire fragment. Such methods may be moreappropriate for some devices used with the current system.

Those of ordinary skill in the art and others will appreciate that thereare many ways of describing the appearance of text fragments. Suchcharacterizations of text fragments may include, but are not limited to,word lengths, relative word lengths, character heights, characterwidths, character shapes, character frequencies, token frequencies, andthe like. In some embodiments, the offsets between matching text tokens(i.e., the number of intervening tokens plus one) are used tocharacterize fragments of text.

Conventional OCR uses knowledge about fonts, letter structure and shapeto attempt to determine characters in scanned text. Embodiments of thepresent invention are different; they employ a variety of methods thatuse the rendered text itself to assist in the recognition process. Theseembodiments use characters (or tokens) to “recognize each other.” Oneway to refer to such self-recognition is “template matching,” and issimilar to “convolution.” To perform such self-recognition, the systemslides a copy of the text horizontally over itself and notes matchingregions of the text images. Prior template matching and convolutiontechniques encompass a variety of related techniques. These techniquesto tokenize and/or recognize characters/tokens will be collectivelyreferred to herein as “autocorrelation,” as the text is used tocorrelate with its own component parts when matching characters/tokens.

When autocorrelating, complete connected regions that match are ofinterest. This occurs when characters (or groups of characters) overlayother instances of the same character (or group). Complete connectedregions that match automatically provide tokenizing of the text intocomponent tokens. As the two copies of the text are slid past eachother, the regions where perfect matching occurs (i.e., all pixels in avertical slice are matched) are noted. When a character/token matchesitself, the horizontal extent of this matching (e.g., the connectedmatching portion of the text) also matches.

Note that at this stage there is no need to determine the actualidentity of each token (i.e., the particular letter, digit or symbol, orgroup of these, that corresponds to the token image), only the offset tothe next occurrence of the same token in the scanned text. The offsetnumber is the distance (number of tokens) to the next occurrence of thesame token. If the token is unique within the text string, the offset iszero (0). The sequence of token offsets thus generated is a signaturethat can be used to identify the scanned text.

In some embodiments, the token offsets determined for a string ofscanned tokens are compared to an index that indexes a corpus ofelectronic documents based upon the token offsets of their contents(Section 4.1.2). In other embodiments, the token offsets determined fora string of scanned tokens are converted to text, and compared to a moreconventional index that indexes a corpus of electronic documents basedupon their contents

As has been noted earlier, a similar token-correlation process may beapplied to speech fragments when the capture process consists of audiosamples of spoken words.

9.7. Font/Character “Self-Recognition”

Conventional template-matching OCR compares scanned images to a libraryof character images. In essence, the alphabet is stored for each fontand newly scanned images are compared to the stored images to findmatching characters. The process generally has an initial delay untilthe correct font has been identified. After that, the OCR process isrelatively quick because most documents use the same font throughout.Subsequent images can therefore be converted to text by comparison withthe most recently identified font library.

The shapes of characters in most commonly used fonts are related. Forexample, in most fonts, the letter “c” and the letter “e” are visuallyrelated—as are “t” and “f,” etc. The OCR process is enhanced by use ofthis relationship to construct templates for letters that have not beenscanned yet. For example, where a reader scans a short string of textfrom a paper document in a previously unencountered font such that thesystem does not have a set of image templates with which to compare thescanned images the system can leverage the probable relationship betweencertain characters to construct the font template library even though ithas not yet encountered all of the letters in the alphabet. The systemcan then use the constructed font template library to recognizesubsequent scanned text and to further refine the constructed fontlibrary.

9.8. Send Anything Unrecognized (Including Graphics) to Server

When images cannot be machine-transcribed into a form suitable for usein a search process, the images themselves can be saved for later use bythe user, for possible manual transcription, or for processing at alater date when different resources may be available to the system.

10. P-Commerce

Many of the actions made possible by the system result in somecommercial transaction taking place. The phrase p-commerce is usedherein to describe commercial activities initiated from paper via thesystem.

10.1. Sales of Documents from their Physical Printed Copies.

When a user captures text from a document, the user may be offered thatdocument for purchase either in paper or electronic form. The user mayalso be offered related documents, such as those quoted or otherwisereferred to in the paper document, or those on a similar subject, orthose by the same author.

10.2. Sales of Anything Else Initiated or Aided by Paper

The capture of text may be linked to other commercial activities in avariety of ways. The captured text may be in a catalog that isexplicitly designed to sell items, in which case the text will beassociated fairly directly with the purchase of an item (Section 18.2).The text may also be part of an advertisement, in which case a sale ofthe item being advertised may ensue.

In other cases, the user captures other text from which their potentialinterest in a commercial transaction may be deduced. A reader of a novelset in a particular country, for example, might be interested in aholiday there. Someone reading a review of a new car might beconsidering purchasing it. The user may capture a particular fragment oftext knowing that some commercial opportunity will be presented to themas a result, or it may be a side-effect of their capture activities.

10.3. Capture of Labels Icons, Serial Numbers, Barcodes on an ItemResulting in a Sale

Sometimes text or symbols are actually printed on an item or itspackaging. An example is the serial number or product id often found ona label on the back or underside of a piece of electronic equipment. Thesystem can offer the user a convenient way to purchase one or more ofthe same items by capturing that text. They may also be offered manuals,support or repair services.

10.4. Contextual Advertisements

In addition to the direct capture of text from an advertisement, thesystem allows for a new kind of advertising which is not necessarilyexplicitly in the rendered document, but is nonetheless based on whatpeople are reading.

10.4.1. Advertising Based on Scan Context and History

In a traditional paper publication, advertisements generally consume alarge amount of space relative to the text of a newspaper article, and alimited number of them can be placed around a particular article. In thedescribed system, advertising can be associated with individual words orphrases, and can selected according to the particular interest the userhas shown by capturing that text and possibly taking into account theirhistory of past scans.

With the described system, it is possible for a purchase to be tied to aparticular printed document and for an advertiser to get significantlymore feedback about the effectiveness of their advertising in particularprint publications.

10.4.2. Advertising Based on User Context and History

The system may gather a large amount of information about other aspectsof a user's context for its own use (Section 13); estimates of thegeographical location of the user are a good example. Such data can alsobe used to tailor the advertising presented to a user of the system.

10.5. Models of Compensation

The system enables some new models of compensation for advertisers andmarketers. The publisher of a printed document containing advertisementsmay receive some income from a purchase that originated from theirdocument. This may be true whether or not the advertisement existed inthe original printed form; it may have been added electronically eitherby the publisher, the advertiser or some third party, and the sources ofsuch advertising may have been subscribed to by the user.

10.5.1. Popularity-Based Compensation

Analysis of the statistics generated by the system can reveal thepopularity of certain parts of a publication (Section 14.2). In anewspaper, for example, it might reveal the amount of time readers spendlooking at a particular page or article, or the popularity of aparticular columnist. In some circumstances, it may be appropriate foran author or publisher to receive compensation based on the activitiesof the readers rather than on more traditional metrics such as wordswritten or number of copies distributed. An author whose work becomes afrequently read authority on a subject might be considered differentlyin future contracts from one whose books have sold the same number ofcopies but are rarely opened. (See also Section 7.6)

10.5.2. Popularity-Based Advertising

Decisions about advertising in a document may also be based onstatistics about the readership. The advertising space around the mostpopular columnists may be sold at a premium rate. Advertisers might evenbe charged or compensated some time after the document is publishedbased on knowledge about how it was received.

10.6. Marketing Based on Life Library

The “Life Library” or scan history described in Sections 6.1 and 16.1can be an extremely valuable source of information about the interestsand habits of a user. Subject to the appropriate consent and privacyissues, such data can inform offers of goods or services to the user.Even in an anonymous form, the statistics gathered can be exceedinglyuseful.

10.7. Sale/Information at Later Date (when Available)

Advertising and other opportunities for commercial transactions may notbe presented to the user immediately at the time of text capture. Forexample, the opportunity to purchase a sequel to a novel may not beavailable at the time the user is reading the novel, but the system maypresent them with that opportunity when the sequel is published.

A user may capture data that relates to a purchase or other commercialtransaction, but may choose not to initiate and/or complete thetransaction at the time the capture is made. In some embodiments, datarelated to captures is stored in a user's Life Library, and these LifeLibrary entries can remain “active” (i.e., capable of subsequentinteractions similar to those available at the time the capture wasmade). Thus a user may review a capture at some later time, andoptionally complete a transaction based on that capture. Because thesystem can keep track of when and where the original capture occurred,all parties involved in the transaction can be properly compensated. Forexample, the author who wrote the story—and the publisher who publishedthe story—that appeared next to the advertisement from which the usercaptured data can be compensated when, six months later, the user visitstheir Life Library, selects that particular capture from the history,and chooses “Purchase this item at Amazon” from the pop-up menu (whichcan be similar or identical to the menu optionally presented at the timeof the capture).

11. Operating System and Application Integration

Modern Operating Systems (OSs) and other software packages have manycharacteristics that can be advantageously exploited for use with thedescribed system, and may also be modified in various ways to provide aneven better platform for its use.

11.1. Incorporation of Scan and Print-Related Information in Metadataand Indexing

New and upcoming file systems and their associated databases often havethe ability to store a variety of metadata associated with each file.Traditionally, this metadata has included such things as the ID of theuser who created the file, the dates of creation, last modification, andlast use. Newer file systems allow such extra information as keywords,image characteristics, document sources and user comments to be stored,and in some systems this metadata can be arbitrarily extended. Filesystems can therefore be used to store information that would be usefulin implementing the current system. For example, the date when a givendocument was last printed can be stored by the file system, as candetails about which text from it has been captured from paper using thedescribed system, and when and by whom.

Operating systems are also starting to incorporate search enginefacilities that allow users to find local files more easily. Thesefacilities can be advantageously used by the system. It means that manyof the search-related concepts discussed in Sections 3 and 4 apply notjust to today's Internet-based and similar search engines, but also toevery personal computer.

In some cases specific software applications will also include supportfor the system above and beyond the facilities provided by the OS.

11.2. OS Support for Capture Devices

As the use of capture devices such as pen scanners becomes increasinglycommon, it will become desirable to build support for them into theoperating system, in much the same way as support is provided for miceand printers, since the applicability of capture devices extends beyonda single software application. The same will be true for other aspectsof the system's operation. Some examples are discussed below. In someembodiments, the entire described system, or the core of it, is providedby the OS. In some embodiments, support for the system is provided byApplication Programming Interfaces (APIs) that can be used by othersoftware packages, including those directly implementing aspects of thesystem.

11.2.1. Support for OCR and Other Recognition Technologies

Most of the methods of capturing text from a rendered document requiresome recognition software to interpret the source data, typically ascanned image or some spoken words, as text suitable for use in thesystem. Some OSs include support for speech or handwriting recognition,though it is less common for OSs to include support for OCR, since inthe past the use of OCR has typically been limited to a small range ofapplications.

As recognition components become part of the OS, they can take betteradvantage of other facilities provided by the OS. Many systems includespelling dictionaries, grammar analysis tools, internationalization andlocalization facilities, for example, all of which can be advantageouslyemployed by the described system for its recognition process, especiallysince they may have been customized for the particular user to includewords and phrases that he/she would commonly encounter.

If the operating system includes full-text indexing facilities, thenthese can also be used to inform the recognition process, as describedin Section 9.3.

11.2.2. Action to be Taken on Scans

If an optical scan or other capture occurs and is presented to the OS,it may have a default action to be taken under those circumstances inthe event that no other subsystem claims ownership of the capture. Anexample of a default action is presenting the user with a choice ofalternatives, or submitting the captured text to the OS's built-insearch facilities.

11.2.3. OS has Default Action for Particular Documents or Document Types

If the digital source of the rendered document is found, the OS may havea standard action that it will take when that particular document, or adocument of that class, is scanned. Applications and other subsystemsmay register with the OS as potential handlers of particular types ofcapture, in a similar manner to the announcement by applications oftheir ability to handle certain file types.

Markup data associated with a rendered document, or with a capture froma document, can include instructions to the operating system to launchspecific applications, pass applications arguments, parameters, or data,etc.

11.2.4. Interpretation of Gestures and Mapping into Standard Actions

In Section 12.1.3 the use of “gestures” is discussed, particularly inthe case of optical scanning, where particular movements made with ahandheld scanner might represent standard actions such as marking thestart and end of a region of text.

This is analogous to actions such as pressing the shift key on akeyboard while using the cursor keys to select a region of text, orusing the wheel on a mouse to scroll a document. Such actions by theuser are sufficiently standard that they are interpreted in asystem-wide way by the OS, thus ensuring consistent behavior. The sameis desirable for scanner gestures and other scanner-related actions.

11.2.5. Set Response to Standard (and Non-Standard) Iconic/Text PrintedMenu Items

In a similar way, certain items of text or other symbols may, whenscanned, cause standard actions to occur, and the OS may provide aselection of these. An example might be that scanning the text “[print]”in any document would cause the OS to retrieve and print a copy of thatdocument. The OS may also provide a way to register such actions andassociate them with particular scans.

11.3. Support in System GUI Components for Typical Scan-InitiatedActivities

Most software applications are based substantially on standard GraphicalUser Interface components provided by the OS.

Use of these components by developers helps to ensure consistentbehavior across multiple packages, for example that pressing theleft-cursor key in any text-editing context should move the cursor tothe left, without every programmer having to implement the samefunctionality independently.

A similar consistency in these components is desirable when theactivities are initiated by text-capture or other aspects of thedescribed system. Some examples are given below.

11.3.1. Interface to Find Particular Text Content

A typical use of the system may be for the user to scan an area of apaper document, and for the system to open the electronic counterpart ina software package that is able to display or edit it, and cause thatpackage to scroll to and highlight the scanned text (Section 12.2.1).The first part of this process, finding and opening the electronicdocument, is typically provided by the OS and is standard acrosssoftware packages. The second part, however—locating a particular pieceof text within a document and causing the package to scroll to it andhighlight it—is not yet standardized and is often implementeddifferently by each package. The availability of a standard API for thisfunctionality could greatly enhance the operation of this aspect of thesystem.

11.3.2. Text Interactions

Once a piece of text has been located within a document, the system maywish to perform a variety of operations upon that text. As an example,the system may request the surrounding text, so that the user's captureof a few words could result in the system accessing the entire sentenceor paragraph containing them. Again, this functionality can be usefullyprovided by the OS rather than being implemented in every piece ofsoftware that handles text.

11.3.3. Contextual (Popup) Menus

Some of the operations that are enabled by the system will require userfeedback, and this may be optimally requested within the context of theapplication handling the data. In some embodiments, the system uses theapplication pop-up menus traditionally associated with clicking theright mouse button on some text. The system inserts extra options intosuch menus, and causes them to be displayed as a result of activitiessuch as scanning a paper document.

11.4. Web/Network Interfaces

In today's increasingly networked world, much of the functionalityavailable on individual machines can also be accessed over a network,and the functionality associated with the described system is noexception. As an example, in an office environment, many paper documentsreceived by a user may have been printed by other users' machines on thesame corporate network. The system on one computer, in response to acapture, may be able to query those other machines for documents whichmay correspond to that capture, subject to the appropriate permissioncontrols.

11.5. Printing of Document Causes Saving

An important factor in the integration of paper and digital documents ismaintaining as much information as possible about the transitionsbetween the two. In some embodiments, the OS keeps a simple record ofwhen any document was printed and by whom. In some embodiments, the OStakes one or more further actions that would make it better suited foruse with the system. Examples include:

Saving the digital rendered version of every document printed along withinformation about the source from which it was printed

Saving a subset of useful information about the printed version—forexample, the fonts used and where the line breaks occur—which might aidfuture scan interpretation

Saving the version of the source document associated with any printedcopy

Indexing the document automatically at the time of printing and storingthe results for future searching

11.6. My (Printed/Scanned) Documents

An OS often maintains certain categories of folders or files that haveparticular significance. A user's documents may, by convention ordesign, be found in a “My Documents” folder, for example. Standardfile-opening dialogs may automatically include a list of recently openeddocuments.

On an OS optimized for use with the described system, such categoriesmay be enhanced or augmented in ways that take into account a user'sinteraction with paper versions of the stored files. Categories such as“My Printed Documents” or “My Recently-Read Documents” might usefully beidentified and incorporated in its operations.

11.7. OS-Level Markup Hierarchies

Since important aspects of the system are typically provided using the“markup” concepts discussed in Section 5, it would clearly beadvantageous to have support for such markup provided by the OS in a waythat was accessible to multiple applications as well as to the OSitself. In addition, layers of markup may be provided by the OS, basedon its own knowledge of documents under its control and the facilitiesit is able to provide.

11.8. Use of OS DRM Facilities

An increasing number of operating systems support some form of “DigitalRights Management”: the ability to control the use of particular dataaccording to the rights granted to a particular user, software entity ormachine. It may inhibit unauthorized copying or distribution of aparticular document, for example.

12. User Interface

The user interface of the system may be entirely on a PC, if the capturedevice is relatively dumb and is connected to it by a cable, or entirelyon the device, if it is sophisticated and with significant processingpower of its own. In some cases, some functionality resides in eachcomponent. Part, or indeed all, of the system's functionality may alsobe implemented on other devices such as mobile phones or PDAs.

The descriptions in the following sections are therefore indications ofwhat may be desirable in certain implementations, but they are notnecessarily appropriate for all and may be modified in several ways.

12.1. On the Capture Device

With all capture devices, but particularly in the case of an opticalscanner, the user's attention will generally be on the device and thepaper at the time of scanning. It is very desirable, then, that anyinput and feedback needed as part of the process of scanning do notrequire the user's attention to be elsewhere, for example on the screenof a computer, more than is necessary.

12.1.1. Feedback on Scanner

A handheld scanner may have a variety of ways of providing feedback tothe user about particular conditions. The most obvious types are directvisual, where the scanner incorporates indicator lights or even a fulldisplay, and auditory, where the scanner can make beeps, clicks or othersounds. Important alternatives include tactile feedback, where thescanner can vibrate, buzz, or otherwise stimulate the user's sense oftouch, and projected feedback, where it indicates a status by projectingonto the paper anything from a colored spot of light to a sophisticateddisplay.

Important immediate feedback that may be provided on the deviceincludes:

feedback on the scanning process—user scanning too fast, at too great anangle, or drifting too high or low on a particular line

sufficient content—enough has been scanned to be pretty certain offinding a match if one exists—important for disconnected operation

-   -   context known—a source of the text has been located    -   unique context known—one unique source of the text has been        located    -   availability of content—indication of whether the content is        freely available to the user, or at a cost

Many of the user interactions normally associated with the later stagesof the system may also take place on the capture device if it hassufficient abilities, for example, to display part or all of a document.

12.1.2. Controls on Scanner

The device may provide a variety of ways for the user to provide inputin addition to basic text capture. Even when the device is in closeassociation with a host machine that has input options such as keyboardsand mice, it can be disruptive for the user to switch back and forthbetween manipulating the scanner and using a mouse, for example.

The handheld scanner may have buttons, scroll/jog-wheels,touch-sensitive surfaces, and/or accelerometers for detecting themovement of the device. Some of these allow a richer set of interactionswhile still holding the scanner.

For example, in response to scanning some text, the system presents theuser with a set of several possible matching documents. The user uses ascroll-wheel on the side of the scanner is to select one from the list,and clicks a button to confirm the selection.

12.1.3. Gestures

The primary reason for moving a scanner across the paper is to capturetext, but some movements may be detected by the device and used toindicate other user intentions. Such movements are referred to herein as“gestures.”

As an example, the user can indicate a large region of text by scanningthe first few words in conventional left-to-right order, and the lastfew in reverse order, i.e. right to left. The user can also indicate thevertical extent of the text of interest by moving the scanner down thepage over several lines. A backwards scan might indicate cancellation ofthe previous scan operation.

12.1.4. Online/Offline Behavior

Many aspects of the system may depend on network connectivity, eitherbetween components of the system such as a scanner and a host laptop, orwith the outside world in the form of a connection to corporatedatabases and Internet search. This connectivity may not be present allthe time, however, and so there will be occasions when part or all ofthe system may be considered to be “offline.” It is desirable to allowthe system to continue to function usefully in those circumstances.

The device may be used to capture text when it is out of contact withother parts of the system. A very simple device may simply be able tostore the image or audio data associated with the capture, ideally witha timestamp indicating when it was captured. The various captures may beuploaded to the rest of the system when the device is next in contactwith it, and handled then. The device may also upload other dataassociated with the captures, for example voice annotations associatedwith optical scans, or location information.

More sophisticated devices may be able to perform some or all of thesystem operations themselves despite being disconnected. Varioustechniques for improving their ability to do so are discussed in Section15.3. Often it will be the case that some, but not all, of the desiredactions can be performed while offline. For example, the text may berecognized, but identification of the source may depend on a connectionto an Internet-based search engine. In some embodiments, the devicetherefore stores sufficient information about how far each operation hasprogressed for the rest of the system to proceed efficiently whenconnectivity is restored.

The operation of the system will, in general, benefit from immediatelyavailable connectivity, but there are some situations in whichperforming several captures and then processing them as a batch can haveadvantages. For example, as discussed in Section 13 below, theidentification of the source of a particular capture may be greatlyenhanced by examining other captures made by the user at approximatelythe same time. In a fully connected system where live feedback is beingprovided to the user, the system is only able to use past captures whenprocessing the current one. If the capture is one of a batch stored bythe device when offline, however, the system will be able to take intoaccount any data available from later captures as well as earlier oneswhen doing its analysis.

12.2. On a Host Device

A scanner will often communicate with some other device, such as a PC,PDA, phone or digital camera to perform many of the functions of thesystem, including more detailed interactions with the user.

12.2.1. Activities Performed in Response to a Capture

When the host device receives a capture, it may initiate a variety ofactivities. An incomplete list of possible activities performed by thesystem after locating and electronic counterpart document associatedwith the capture and a location within that document follows.

The details of the capture may be stored in the user's history. (Section6.1)

The document may be retrieved from local storage or a remote location.(Section 8)

The operating system's metadata and other records associated with thedocument may be updated. (Section 11.1)

Markup associated with the document may be examined to determine thenext relevant operations. (Section 5)

A software application may be started to edit, view or otherwise operateon the document. The choice of application may depend on the sourcedocument, or on the contents of the scan, or on some other aspect of thecapture. (Section 11.2.2, 11.2.3)

The application may scroll to, highlight, move the insertion point to,or otherwise indicate the location of the capture. (Section 11.3)

The precise bounds of the captured text may be modified, for example toselect whole words, sentences or paragraphs around the captured text.(Section 11.3.2)

The user may be given the option to copy the capture text to theclipboard or perform other standard operating system orapplication-specific operations upon it.

Annotations may be associated with the document or the captured text.These may come from immediate user input, or may have been capturedearlier, for example in the case of voice annotations associated with anoptical scan. (Section 19.4)

Markup may be examined to determine a set of further possible operationsfor the user to select.

12.2.2. Contextual Popup Menus

Sometimes the appropriate action to be taken by the system will beobvious, but sometimes it will require a choice to be made by the user.One good way to do this is through the use of “popup menus” or, in caseswhere the content is also being displayed on a screen, with so-called“contextual menus” that appear close to the content. (See Section11.3.3). In some embodiments, the scanner device projects a popup menuonto the paper document. A user may select from such menus usingtraditional methods such as a keyboard and mouse, or by using controlson the capture device (Section 12.1.2), gestures (Section 12.1.3), or byinteracting with the computer display using the scanner (Section12.2.4). In some embodiments, the popup menus which can appear as aresult of a capture include default items representing actions whichoccur if the user does not respond—for example, if the user ignores themenu and makes another capture.

12.2.3. Feedback on Disambiguation

When a user starts capturing text, there will initially be severaldocuments or other text locations that it could match. As more text iscaptured, and other factors are taken into account (Section 13), thenumber of candidate locations will decrease until the actual location isidentified, or further disambiguation is not possible without userinput. In some embodiments, the system provides a real-time display ofthe documents or the locations found, for example in list,thumbnail-image or text-segment form, and for the number of elements inthat display to reduce in number as capture continues. In someembodiments, the system displays thumbnails of all candidate documents,where the size or position of the thumbnail is dependent on theprobability of it being the correct match.

When a capture is unambiguously identified, this fact may be emphasizedto the user, for example using audio feedback.

Sometimes the text captured will occur in many documents and will berecognized to be a quotation. The system may indicate this on thescreen, for example by grouping documents containing a quoted referencearound the original source document.

12.2.4. Scanning from Screen

Some optical scanners may be able to capture text displayed on a screenas well as on paper. Accordingly, the term rendered document is usedherein to indicate that printing onto paper is not the only form ofrendering, and that the capture of text or symbols for use by the systemmay be equally valuable when that text is displayed on an electronicdisplay.

The user of the described system may be required to interact with acomputer screen for a variety of other reasons, such as to select from alist of options. It can be inconvenient for the user to put down thescanner and start using the mouse or keyboard. Other sections havedescribed physical controls on the scanner (Section 12.1.2) or gestures(Section 12.1.3) as methods of input which do not require this change oftool, but using the scanner on the screen itself to scan some text orsymbol is an important alternative provided by the system.

In some embodiments, the optics of the scanner allow it to be used in asimilar manner to a light-pen, directly sensing its position on thescreen without the need for actual scanning of text, possibly with theaid of special hardware or software on the computer.

13. Context Interpretation

An important aspect of the described system is the use of other factors,beyond the simple capture of a string of text, to help identify thedocument in use. A capture of a modest amount of text may often identifythe document uniquely, but in many situations it will identify a fewcandidate documents. One solution is to prompt the user to confirm thedocument being scanned, but a preferable alternative is to make use ofother factors to narrow down the possibilities automatically. Suchsupplemental information can dramatically reduce the amount of text thatneeds to be captured and/or increase the reliability and speed withwhich the location in the electronic counterpart can be identified. Thisextra material is referred to as “context,” and it was discussed brieflyin Section 4.2.2. We now consider it in more depth.

13.1. System and Capture Context

Perhaps the most important example of such information is the user'scapture history.

It is highly probable that any given capture comes from the samedocument as the previous one, or from an associated document, especiallyif the previous capture took place in the last few minutes (Section6.1.2). Conversely, if the system detects that the font has changedbetween two scans, it is more likely that they are from differentdocuments.

Also useful are the user's longer-term capture history and readinghabits. These can also be used to develop a model of the user'sinterests and associations.

13.2. User's Real-World Context

Another example of useful context is the user's geographical location. Auser in Paris is much more likely to be reading Le Monde than theSeattle Times, for example. The timing, size and geographicaldistribution of printed versions of the documents can therefore beimportant, and can to some degree be deduced from the operation of thesystem.

The time of day may also be relevant, for example in the case of a userwho always reads one type of publication on the way to work, and adifferent one at lunchtime or on the train going home.

13.3. Related Digital Context

The user's recent use of electronic documents, including those searchedfor or retrieved by more conventional means, can also be a helpfulindicator.

In some cases, such as on a corporate network, other factors may beusefully considered:

Which documents have been printed recently?

Which documents have been modified recently on the corporate fileserver?

Which documents have been emailed recently?

All of these examples might suggest that a user was more likely to bereading a paper version of those documents. In contrast, if therepository in which a document resides can affirm that the document hasnever been printed or sent anywhere where it might have been printed,then it can be safely eliminated in any searches originating from paper.

13.4. Other Statistics—The Global Context

Section 14 covers the analysis of the data stream resulting frompaper-based searches, but it should be noted here that statistics aboutthe popularity of documents with other readers, about the timing of thatpopularity, and about the parts of documents most frequently scanned areall examples of further factors which can be beneficial in the searchprocess. The system brings the possibility of Google-type page-rankingto the world of paper.

See also Section 4.2.2 for some other implications of the use of contextfor search engines.

14. Data-Stream Analysis

The use of the system generates an exceedingly valuable data-stream as aside effect. This stream is a record of what users are reading and when,and is in many cases a record of what they find particularly valuable inthe things they read. Such data has never really been available beforefor paper documents.

Some ways in which this data can be useful for the system, and for theuser of the system, are described in Section 6.1. This sectionconcentrates on its use for others. There are, of course, substantialprivacy issues to be considered with any distribution of data about whatpeople are reading, but such issues as preserving the anonymity of dataare well known to those of skill in the art.

14.1. Document Tracking

When the system knows which documents any given user is reading, it canalso deduce who is reading any given document. This allows the trackingof a document through an organization, to allow analysis, for example,of who is reading it and when, how widely it was distributed, how longthat distribution took, and who has seen current versions while othersare still working from out-of-date copies.

For published documents that have a wider distribution, the tracking ofindividual copies is more difficult, but the analysis of thedistribution of readership is still possible.

14.2. Read Ranking—Popularity of Documents and Sub-Regions

In situations where users are capturing text or other data that is ofparticular interest to them, the system can deduce the popularity ofcertain documents and of particular sub-regions of those documents. Thisforms a valuable input to the system itself (Section 4.2.2) and animportant source of information for authors, publishers and advertisers(Section 7.6, Section 10.5). This data is also useful when integrated insearch engines and search indices—for example, to assist in rankingsearch results for queries coming from rendered documents, and/or toassist in ranking conventional queries typed into a web browser.

14.3. Analysis of Users—Building Profiles

Knowledge of what a user is reading enables the system to create a quitedetailed model of the user's interests and activities. This can beuseful on an abstract statistical basis—“35% of users who buy thisnewspaper also read the latest book by that author”—but it can alsoallow other interactions with the individual user, as discussed below.

14.3.1. Social Networking

One example is connecting one user with others who have relatedinterests. These may be people already known to the user. The system mayask a university professor, “Did you know that your colleague at XYZUniversity has also just read this paper?” The system may ask a user,“Do you want to be linked up with other people in your neighborhood whoare also how reading Jane Eyre?” Such links may be the basis for theautomatic formation of book clubs and similar social structures, eitherin the physical world or online.

14.3.2. Marketing

Section 10.6 has already mentioned the idea of offering products andservices to an individual user based on their interactions with thesystem. Current online booksellers, for example, often makerecommendations to a user based on their previous interactions with thebookseller. Such recommendations become much more useful when they arebased on interactions with the actual books.

14.4. Marketing Based on Other Aspects of the Data-Stream

We have discussed some of the ways in which the system may influencethose publishing documents, those advertising through them, and othersales initiated from paper (Section 10). Some commercial activities mayhave no direct interaction with the paper documents at all and yet maybe influenced by them. For example, the knowledge that people in onecommunity spend more time reading the sports section of the newspaperthan they do the financial section might be of interest to somebodysetting up a health club.

14.5. Types of Data that May be Captured

In addition to the statistics discussed, such as who is reading whichbits of which documents, and when and where, it can be of interest toexamine the actual contents of the text captured, regardless of whetheror not the document has been located.

In many situations, the user will also not just be capturing some text,but will be causing some action to occur as a result. It might beemailing a reference to the document to an acquaintance, for example.Even in the absence of information about the identity of the user or therecipient of the email, the knowledge that somebody considered thedocument worth emailing is very useful.

In addition to the various methods discussed for deducing the value of aparticular document or piece of text, in some circumstances the userwill explicitly indicate the value by assigning it a rating.

Lastly, when a particular set of users are known to form a group, forexample when they are known to be employees of a particular company, theaggregated statistics of that group can be used to deduce the importanceof a particular document to that group.

15. Device Features and Functions

A capture device for use with the system needs little more than a way ofcapturing text from a rendered version of the document. As describedearlier (Section 1.2), this capture may be achieved through a variety ofmethods including taking a photograph of part of the document or typingsome words into a mobile phone keypad. This capture may be achievedusing a small hand-held optical scanner capable of recording a line ortwo of text at a time, or an audio capture device such as avoice-recorder into which the user is reading text from the document.The device used may be a combination of these—an optical scanner whichcould also record voice annotations, for example—and the capturingfunctionality may be built into some other device such as a mobilephone, PDA, digital camera or portable music player.

15.1. Input and Output

Many of the possibly beneficial additional input and output facilitiesfor such a device have been described in Section 12.1. They includebuttons, scroll-wheels and touch-pads for input, and displays, indicatorlights, audio and tactile transducers for output. Sometimes the devicewill incorporate many of these, sometimes very few. Sometimes thecapture device will be able to communicate with another device thatalready has them (Section 15.6), for example using a wireless link, andsometimes the capture functionality will be incorporated into such otherdevice (Section 15.7).

15.2. Connectivity

In some embodiments, the device implements the majority of the systemitself. In some embodiments, however, it often communicates with a PC orother computing device and with the wider world using communicationsfacilities.

Often these communications facilities are in the form of ageneral-purpose data network such as Ethernet, 802.11 or UWB or astandard peripheral-connecting network such as USB, IEEE-1394(Firewire), Bluetooth™ or infra-red. When a wired connection such asFirewire or USB is used, the device may receive electrical power thoughthe same connection. In some circumstances, the capture device mayappear to a connected machine to be a conventional peripheral such as aUSB storage device.

Lastly, the device may in some circumstances “dock” with another device,either to be used in conjunction with that device or for convenientstorage.

15.3. Caching and Other Online/Offline Functionality

Sections 3.5 and 12.1.4 have raised the topic of disconnected operation.When a capture device has a limited subset of the total system'sfunctionality, and is not in communication with the other parts of thesystem, the device can still be useful, though the functionalityavailable will sometimes be reduced. At the simplest level, the devicecan record the raw image or audio data being captured and this can beprocessed later. For the user's benefit, however, it can be important togive feedback where possible about whether the data captured is likelyto be sufficient for the task in hand, whether it can be recognized oris likely to be recognizable, and whether the source of the data can beidentified or is likely to be identifiable later. The user will thenknow whether their capturing activity is worthwhile. Even when all ofthe above are unknown, the raw data can still be stored so that, at thevery least, the user can refer to them later. The user may be presentedwith the image of a scan, for example, when the scan cannot berecognized by the OCR process.

To illustrate some of the range of options available, both a ratherminimal optical scanning device and then a much more full-featured oneare described below. Many devices occupy a middle ground between thetwo.

15.3.1. The SimpleScanner—A Low-End Offline Example

The SimpleScanner has a scanning head able to read pixels from the pageas it is moved along the length of a line of text. It can detect itsmovement along the page and record the pixels with some informationabout the movement. It also has a clock, which allows each scan to betime-stamped. The clock is synchronized with a host device when theSimpleScanner has connectivity. The clock may not represent the actualtime of day, but relative times may be determined from it so that thehost can deduce the actual time of a scan, or at worst the elapsed timebetween scans.

The SimpleScanner does not have sufficient processing power to performany OCR itself, but it does have some basic knowledge about typicalword-lengths, word-spacings, and their relationship to font size. It hassome basic indicator lights which tell the user whether the scan islikely to be readable, whether the head is being moved too fast, tooslowly or too inaccurately across the paper, and when it determines thatsufficient words of a given size are likely to have been scanned for thedocument to be identified.

The SimpleScanner has a USB connector and can be plugged into the USBport on a computer, where it will be recharged. To the computer itappears to be a USB storage device on which time-stamped data files havebeen recorded, and the rest of the system software takes over from thispoint.

15.3.2. The SuperScanner—A High-End Offline Example

The SuperScanner also depends on connectivity for its full operation,but it has a significant amount of on-board storage and processing whichcan help it make better judgments about the data captured while offline.

As it moves along the line of text, the captured pixels are stitchedtogether and passed to an OCR engine that attempts to recognize thetext. A number of fonts, including those from the user's most-readpublications, have been downloaded to it to help perform this task, ashas a dictionary that is synchronized with the user's spelling-checkerdictionary on their PC and so contains many of the words they frequentlyencounter. Also stored on the scanner is a list of words and phraseswith the typical frequency of their use—this may be combined with thedictionary. The scanner can use the frequency statistics both to helpwith the recognition process and also to inform its judgment about whena sufficient quantity of text has been captured; more frequently usedphrases are less likely to be useful as the basis for a search query.

In addition, the full index for the articles in the recent issues of thenewspapers and periodicals most commonly read by the user are stored onthe device, as are the indices for the books the user has recentlypurchased from an online bookseller, or from which the user has scannedanything within the last few months. Lastly, the titles of severalthousand of the most popular publications which have data available forthe system are stored so that, in the absence of other information theuser can scan the title and have a good idea as to whether or notcaptures from a particular work are likely to be retrievable inelectronic form later

During the scanning process, the system informs user that the captureddata has been of sufficient quality and of a sufficient nature to makeit probable that the electronic copy can be retrieved when connectivityis restored. Often the system indicates to the user that the scan isknown to have been successful and that the context has been recognizedin one of the on-board indices, or that the publication concerned isknown to be making its data available to the system, so the laterretrieval ought to be successful.

The SuperScanner docks in a cradle connected to a PC's Firewire or USBport, at which point, in addition to the upload of captured data, itsvarious onboard indices and other databases are updated based on recentuser activity and new publications. It also has the facility to connectto wireless public networks or to communicate via Bluetooth to a mobilephone and thence with the public network when such facilities areavailable.

15.4. Features for Optical Scanning

We now consider some of the features that may be particularly desirablein an optical scanner device.

15.4.1. Flexible Positioning and Convenient Optics

One of the reasons for the continuing popularity of paper is the ease ofits use in a wide variety of situations where a computer, for example,would be impractical or inconvenient. A device intended to capture asubstantial part of a user's interaction with paper should therefore besimilarly convenient in use. This has not been the case for scanners inthe past; even the smallest hand-held devices have been somewhatunwieldy. Those designed to be in contact with the page have to be heldat a precise angle to the paper and moved very carefully along thelength of the text to be scanned. This is acceptable when scanning abusiness report on an office desk, but may be impractical when scanninga phrase from a novel while waiting for a train. Scanners based oncamera-type optics that operate at a distance from the paper maysimilarly be useful in some circumstances.

Some embodiments of the system use a scanner that scans in contact withthe paper, and which, instead of lenses, uses an image conduit a bundleof optical fibers to transmit the image from the page to the opticalsensor device. Such a device can be shaped to allow it to be held in anatural position; for example, in some embodiments, the part in contactwith the page is wedge-shaped, allowing the user's hand to move morenaturally over the page in a movement similar to the use of ahighlighter pen. The conduit is either in direct contact with the paperor in close proximity to it, and may have a replaceable transparent tipthat can protect the image conduit from possible damage. As has beenmentioned in Section 12.2.4, the scanner may be used to scan from ascreen as well as from paper, and the material of the tip can be chosento reduce the likelihood of damage to such displays.

Lastly, some embodiments of the device will provide feedback to the userduring the scanning process which will indicate through the use oflight, sound or tactile feedback when the user is scanning too fast, tooslow, too unevenly or is drifting too high or low on the scanned line.

15.5. Security, Identity, Authentication, Personalization and Billing

As described in Section 6, the capture device may form an important partof identification and authorization for secure transactions, purchases,and a variety of other operations. It may therefore incorporate, inaddition to the circuitry and software required for such a role, varioushardware features that can make it more secure, such as a smartcardreader, RFID, or a keypad on which to type a PIN.

It may also include various biometric sensors to help identify the user.In the case of an optical scanner, for example, the scanning head mayalso be able to read a fingerprint. For a voice recorder, the voicepattern of the user may be used.

15.6. Device Associations

In some embodiments, the device is able to form an association withother nearby devices to increase either its own or their functionality.In some embodiments, for example, it uses the display of a nearby PC orphone to give more detailed feedback about its operation, or uses theirnetwork connectivity. The device may, on the other hand, operate in itsrole as a security and identification device to authenticate operationsperformed by the other device. Or it may simply form an association inorder to function as a peripheral to that device.

An interesting aspect of such associations is that they may be initiatedand authenticated using the capture facilities of the device. Forexample, a user wishing to identify themselves securely to a publiccomputer terminal may use the scanning facilities of the device to scana code or symbol displayed on a particular area of the terminal's screenand so effect a key transfer. An analogous process may be performedusing audio signals picked up by a voice-recording device.

15.7. Integration with Other Devices

In some embodiments, the functionality of the capture device isintegrated into some other device that is already in use. The integrateddevices may be able to share a power supply, data capture and storagecapabilities, and network interfaces. Such integration may be donesimply for convenience, to reduce cost, or to enable functionality thatwould not otherwise be available.

Some examples of devices into which the capture functionality can beintegrated include:

an existing peripheral such as a mouse, a stylus, a USB “webcam” camera,a Bluetooth™ headset or a remote control

another processing/storage device, such as a PDA, an MP3 player, a voicerecorder, a digital camera or a mobile phone

other often-carried items, just for convenience—a watch, a piece ofjewelry, a pen, a car key fob

15.7.1. Mobile Phone Integration

As an example of the benefits of integration, we consider the use of amodified mobile phone as the capture device.

In some embodiments, the phone hardware is not modified to support thesystem, such as where the text capture can be adequately done throughvoice recognition, where they can either be processed by the phoneitself, or handled by a system at the other end of a telephone call, orstored in the phone's memory for future processing. Many modern phoneshave the ability to download software that could implement some parts ofthe system. Such voice capture is likely to be suboptimal in manysituations, however, for example when there is substantial backgroundnoise, and accurate voice recognition is a difficult task at the best oftimes. The audio facilities may best be used to capture voiceannotations.

In some embodiments, the camera built into many mobile phones is used tocapture an image of the text. The phone display, which would normallyact as a viewfinder for the camera, may overlay on the live camera imageinformation about the quality of the image and its suitability for OCR,which segments of text are being captured, and even a transcription ofthe text if the OCR can be performed on the phone.

In some embodiments, the phone is modified to add dedicated capturefacilities, or to provide such functionality in a clip-on adaptor or aseparate Bluetooth-connected peripheral in communication with the phone.Whatever the nature of the capture mechanism, the integration with amodern cellphone has many other advantages. The phone has connectivitywith the wider world, which means that queries can be submitted toremote search engines or other parts of the system, and copies ofdocuments may be retrieved for immediate storage or viewing. A phonetypically has sufficient processing power for many of the functions ofthe system to be performed locally, and sufficient storage to capture areasonable amount of data. The amount of storage can also often beexpanded by the user. Phones have reasonably good displays and audiofacilities to provide user feedback, and often a vibrate function fortactile feedback. They also have good power supplies.

Most significantly of all, they are a device that most users are alreadycarrying.

Part III—Example Applications of the System

This section lists example uses of the system and applications that maybe built on it. This list is intended to be purely illustrative and inno sense exhaustive.

16. Personal Applications

16.1. Life Library

The Life Library (see also Section 6.1.1) is a digital archive of anyimportant documents that the subscriber wishes to save and is a set ofembodiments of services of this system. Important books, magazinearticles, newspaper clippings, etc., can all be saved in digital form inthe Life Library. Additionally, the subscriber's annotations, comments,and notes can be saved with the documents. The Life Library can beaccessed via the Internet and World Wide Web.

The system creates and manages the Life Library document archive forsubscribers. The subscriber indicates which documents the subscriberwishes to have saved in his life library by scanning information fromthe document or by otherwise indicating to the system that theparticular document is to be added to the subscriber's Life Library. Thescanned information is typically text from the document but can also bea barcode or other code identifying the document. The system accepts thecode and uses it to identify the source document. After the document isidentified the system can store either a copy of the document in theuser's Life Library or a link to a source where the document may beobtained.

One embodiment of the Life Library system can check whether thesubscriber is authorized to obtain the electronic copy. For example, ifa reader scans text or an identifier from a copy of an article in theNew York Times (NYT) so that the article will be added to the reader'sLife Library, the Life Library system will verify with the NYT whetherthe reader is subscribed to the online version of the NYT; if so, thereader gets a copy of the article stored in his Life Library account; ifnot, information identifying the document and how to order it is storedin his Life Library account.

In some embodiments, the system maintains a subscriber profile for eachsubscriber that includes access privilege information. Document accessinformation can be compiled in several ways, two of which are: 1) thesubscriber supplies the document access information to the Life Librarysystem, along with his account names and passwords, etc., or 2) the LifeLibrary service provider queries the publisher with the subscriber'sinformation and the publisher responds by providing access to anelectronic copy if the Life Library subscriber is authorized to accessthe material. If the Life Library subscriber is not authorized to havean electronic copy of the document, the publisher provides a price tothe Life Library service provider, which then provides the customer withthe option to purchase the electronic document. If so, the Life Libraryservice provider either pays the publisher directly and bills the LifeLibrary customer later or the Life Library service provider immediatelybills the customer's credit card for the purchase. The Life Libraryservice provider would get a percentage of the purchase price or a smallfixed fee for facilitating the transaction.

The system can archive the document in the subscriber's personal libraryand/or any other library to which the subscriber has archivalprivileges. For example, as a user scans text from a printed document,the Life Library system can identify the rendered document and itselectronic counterpart. After the source document is identified, theLife Library system might record information about the source documentin the user's personal library and in a group library to which thesubscriber has archival privileges. Group libraries are collaborativearchives such as a document repository for: a group working together ona project, a group of academic researchers, a group web log, etc.

The life library can be organized in many ways: chronologically, bytopic, by level of the subscriber's interest, by type of publication(newspaper, book, magazine, technical paper, etc.), where read, whenread, by ISBN or by Dewey decimal, etc. In one alternative, the systemcan learn classifications based on how other subscribers have classifiedthe same document. The system can suggest classifications to the user orautomatically classify the document for the user.

In various embodiments, annotations may be inserted directly into thedocument or may be maintained in a separate file. For example, when asubscriber scans text from a newspaper article, the article is archivedin his Life Library with the scanned text highlighted. Alternatively,the article is archived in his Life Library along with an associatedannotation file (thus leaving the archived document unmodified).Embodiments of the system can keep a copy of the source document in eachsubscriber's library, a copy in a master library that many subscriberscan access, or link to a copy held by the publisher.

In some embodiments, the Life Library stores only the user'smodifications to the document (e.g., highlights, etc.) and a link to anonline version of the document (stored elsewhere). The system or thesubscriber merges the changes with the document when the subscribersubsequently retrieves the document.

If the annotations are kept in a separate file, the source document andthe annotation file are provided to the subscriber and the subscribercombines them to create a modified document. Alternatively, the systemcombines the two files prior to presenting them to the subscriber. Inanother alternative, the annotation file is an overlay to the documentfile and can be overlaid on the document by software in the subscriber'scomputer.

Subscribers to the Life Library service pay a monthly fee to have thesystem maintain the subscriber's archive. Alternatively, the subscriberpays a small amount (e.g., a micro-payment) for each document stored inthe archive. Alternatively, the subscriber pays to access thesubscriber's archive on a per-access fee. Alternatively, subscribers cancompile libraries and allow others to access the materials/annotationson a revenue share model with the Life Library service provider andcopyright holders. Alternatively, the Life Library service providerreceives a payment from the publisher when the Life Library subscriberorders a document (a revenue share model with the publisher, where theLife Library service provider gets a share of the publisher's revenue).

In some embodiments, the Life Library service provider acts as anintermediary between the subscriber and the copyright holder (orcopyright holder's agent, such as the Copyright Clearance Center, a.k.a.CCC) to facilitate billing and payment for copyrighted materials. TheLife Library service provider uses the subscriber's billing informationand other user account information to provide this intermediationservice. Essentially, the Life Library service provider leverages thepre-existing relationship with the subscriber to enable purchase ofcopyrighted materials on behalf of the subscriber.

In some embodiments, the Life Library system can store excerpts fromdocuments. For example, when a subscriber scans text from a paperdocument, the regions around the scanned text are excerpted and placedin the Life Library, rather than the entire document being archived inthe life library. This is especially advantageous when the document islong because preserving the circumstances of the original scan preventsthe subscriber from re-reading the document to find the interestingportions. Of course, a hyperlink to the entire electronic counterpart ofthe paper document can be included with the excerpt materials.

In some embodiments, the system also stores information about thedocument in the Life Library, such as author, publication title,publication date, publisher, copyright holder (or copyright holder'slicensing agent), ISBN, links to public annotations of the document,readrank, etc. Some of this additional information about the document isa form of paper document metadata. Third parties may create publicannotation files for access by persons other than themselves, such thegeneral public. Linking to a third party's commentary on a document isadvantageous because reading annotation files of other users enhancesthe subscriber's understanding of the document.

In some embodiments, the system archives materials by class. Thisfeature allows a Life Library subscriber to quickly store electroniccounterparts to an entire class of paper documents without access toeach paper document. For example, when the subscriber scans some textfrom a copy of National Geographic magazine, the system provides thesubscriber with the option to archive all back issues of the NationalGeographic. If the subscriber elects to archive all back issues, theLife Library service provider would then verify with the NationalGeographic Society whether the subscriber is authorized to do so. Ifnot, the Life Library service provider can mediate the purchase of theright to archive the National Geographic magazine collection.

16.2. Life Saver

A variation on, or enhancement of, the Life Library concept is the “LifeSaver,” where the system uses the text captured by a user to deduce moreabout their other activities. The scanning of a menu from a particularrestaurant, a program from a particular theater performance, a timetableat a particular railway station, or an article from a local newspaperallows the system to make deductions about the user's location andsocial activities, and could construct an automatic diary for them, forexample as a website. The user would be able to edit and modify thediary, add additional materials such as photographs and, of course, lookagain at the items scanned.

17. Academic Applications

Portable scanners supported by the described system have many compellinguses in the academic setting. They can enhance student/teacherinteraction and augment the learning experience. Among other uses,students can annotate study materials to suit their unique needs;teachers can monitor classroom performance; and teachers canautomatically verify source materials cited in student assignments.

17.1. Children's Books

A child's interaction with a paper document, such as a book, ismonitored by a literacy acquisition system that employs a specific setof embodiments of this system. The child uses a portable scanner thatcommunicates with other elements of the literacy acquisition system. Inaddition to the portable scanner, the literacy acquisition systemincludes a computer having a display and speakers, and a databaseaccessible by the computer. The scanner is coupled with the computer(hardwired, short range RF, etc.). When the child sees an unknown wordin the book, the child scans it with the scanner. In one embodiment, theliteracy acquisition system compares the scanned text with the resourcesin its database to identify the word. The database includes adictionary, thesaurus, and/or multimedia files (e.g., sound, graphics,etc.). After the word has been identified, the system uses the computerspeakers to pronounce the word and its definition to the child. Inanother embodiment, the word and its definition are displayed by theliteracy acquisition system on the computer's monitor. Multimedia filesabout the scanned word can also be played through the computer's monitorand speakers. For example, if a child reading “Goldilocks and the ThreeBears” scanned the word “bear,” the system might pronounce the word“bear” and play a short video about bears on the computer's monitor. Inthis way, the child learns to pronounce the written word and is visuallytaught what the word means via the multimedia presentation.

The literacy acquisition system provides immediate auditory and/orvisual information to enhance the learning process. The child uses thissupplementary information to quickly acquire a deeper understanding ofthe written material. The system can be used to teach beginning readersto read, to help children acquire a larger vocabulary, etc. This systemprovides the child with information about words with which the child isunfamiliar or about which the child wants more information.

17.2. Literacy Acquisition

In some embodiments, the system compiles personal dictionaries. If thereader sees a word that is new, interesting, or particularly useful ortroublesome, the reader saves it (along with its definition) to acomputer file. This computer file becomes the reader's personalizeddictionary. This dictionary is generally smaller in size than a generaldictionary so can be downloaded to a mobile station or associated deviceand thus be available even when the system isn't immediately accessible.In some embodiments, the personal dictionary entries include audio filesto assist with proper word pronunciation and information identifying thepaper document from which the word was scanned.

In some embodiments, the system creates customized spelling andvocabulary tests for students. For example, as a student reads anassignment, the student may scan unfamiliar words with the portablescanner. The system stores a list of all the words that the student hasscanned. Later, the system administers a customized spelling/vocabularytest to the student on an associated monitor (or prints such a test onan associated printer).

17.3. Music Teaching

The arrangement of notes on a musical staff is similar to thearrangement of letters in a line of text. The same scanning devicediscussed for capturing text in this system can be used to capture musicnotation, and an analogous process of constructing a search againstdatabases of known musical pieces would allow the piece from which thecapture occurred to be identified which can then be retrieved, played,or be the basis for some further action.

17.4. Detecting Plagiarism

Teachers can use the system to detect plagiarism or to verify sources byscanning text from student papers and submitting the scanned text to thesystem. For example, a teacher who wishes to verify that a quote in astudent paper came from the source that the student cited can scan aportion of the quote and compare the title of the document identified bythe system with the title of the document cited by the student.Likewise, the system can use scans of text from assignments submitted asthe student's original work to reveal if the text was instead copied.

17.5. Enhanced Textbook

In some embodiments, capturing text from an academic textbook linksstudents or staff to more detailed explanations, further exercises,student and staff discussions about the material, related example pastexam questions, further reading on the subject, recordings of thelectures on the subject, and so forth. (See also Section 7.1.)

17.6. Language Learning

In some embodiments, the system is used to teach foreign languages.Scanning a Spanish word, for example, might cause the word to be readaloud in Spanish along with its definition in English.

The system provides immediate auditory and/or visual information toenhance the new language acquisition process. The reader uses thissupplementary information to acquire quickly a deeper understanding ofthe material. The system can be used to teach beginning students to readforeign languages, to help students acquire a larger vocabulary, etc.The system provides information about foreign words with which thereader is unfamiliar or for which the reader wants more information.

Reader interaction with a paper document, such as a newspaper or book,is monitored by a language skills system. The reader has a portablescanner that communicates with the language skills system. In someembodiments, the language skills system includes a computer having adisplay and speakers, and a database accessible by the computer. Thescanner communicates with the computer (hardwired, short range RF,etc.). When the reader sees an unknown word in an article, the readerscans it with the scanner. The database includes a foreign languagedictionary, thesaurus, and/or multimedia files (sound, graphics, etc.).In some embodiments, the system compares the scanned text with theresources in its database to identify the scanned word. After the wordhas been identified, the system uses the computer speakers to pronouncethe word and its definition to the reader. In some embodiments, the wordand its definition are both displayed on the computer's monitor.Multimedia files about grammar tips related to the scanned word can alsobe played through the computer's monitor and speakers. For example, ifthe words “to speak” are scanned, the system might pronounce the word“hablar,” play a short audio clip that demonstrates the proper Spanishpronunciation, and display a complete list of the various conjugationsof “hablar.” In this way, the student learns to pronounce the writtenword, is visually taught the spelling of the word via the multimediapresentation, and learns how to conjugate the verb. The system can alsopresent grammar tips about the proper usage of “hablar” along withcommon phrases.

In some embodiments, the user scans a word or short phrase from arendered document in a language other than the user's native language(or some other language that the user knows reasonably well). In someembodiments, the system maintains a prioritized list of the user's“preferred” languages. The system identifies the electronic counterpartof the rendered document, and determines the location of the scan withinthe document. The system also identifies a second electronic counterpartof the document that has been translated into one of the user'spreferred languages, and determines the location in the translateddocument corresponding to the location of the scan in the originaldocument. When the corresponding location is not known precisely, thesystem identifies a small region (e.g., a paragraph) that includes thecorresponding location of the scanned location. The correspondingtranslated location is then presented to the user. This provides theuser with a precise translation of the particular usage at the scannedlocation, including any slang or other idiomatic usage that is oftendifficult to accurately translate on a word-by-word basis.

17.7. Gathering Research Materials

A user researching a particular topic may encounter all sorts ofmaterial, both in print and on screen, which they might wish to recordas relevant to the topic in some personal archive. The system wouldenable this process to be automatic as a result of scanning a shortphrase in any piece of material, and could also create a bibliographysuitable for insertion into a publication on the subject.

18. Commercial Applications

Obviously, commercial activities could be made out of almost any processdiscussed in this document, but here we concentrate on a few obviousrevenue streams.

18.1. Fee-Based Searching and Indexing

Conventional Internet search engines typically provide free search ofelectronic documents, and also make no charge to the content providersfor including their content in the index. In some embodiments, thesystem provides for charges to users and/or payments to search enginesand/or content providers in connection with the operation and use of thesystem.

In some embodiments, subscribers to the system's services pay a fee forsearches originating from scans of paper documents. For example, astockbroker may be reading a Wall Street Journal article about a newproduct offered by Company X. By scanning the Company X name from thepaper document and agreeing to pay the necessary fees, the stockbrokeruses the system to search special or proprietary databases to obtainpremium information about the company, such as analyst's reports. Thesystem can also make arrangements to have priority indexing of thedocuments most likely to be read in paper form, for example by makingsure all of the newspapers published on a particular day are indexed andavailable by the time they hit the streets.

Content providers may pay a fee to be associated with certain terms insearch queries submitted from paper documents. For example, in oneembodiment, the system chooses a most preferred content provider basedon additional context about the provider (the context being, in thiscase, that the content provider has paid a fee to be moved up theresults list). In essence, the search provider is adjusting paperdocument search results based on pre-existing financial arrangementswith a content provider. See also the description of keywords and keyphrases in Section 5.2.

Where access to particular content is to be restricted to certain groupsof people (such as clients or employees), such content may be protectedby a firewall and thus not generally indexable by third parties. Thecontent provider may nonetheless wish to provide an index to theprotected content. In such a case, the content provider can pay aservice provider to provide the content provider's index to systemsubscribers. For example, a law firm may index all of a client'sdocuments. The documents are stored behind the law firm's firewall.However, the law firm wants its employees and the client to have accessto the documents through the portable scanner so it provides the index(or a pointer to the index) to the service provider, which in turnsearches the law firm's index when employees or clients of the law firmsubmit paper-scanned search terms via their portable scanners. The lawfirm can provide a list of employees and/or clients to the serviceprovider's system to enable this function or the system can verifyaccess rights by querying the law firm prior to searching the law firm'sindex. Note that in the preceding example, the index provided by the lawfirm is only of that client's documents, not an index of all documentsat the law firm. Thus, the service provider can only grant the lawfirm's clients access to the documents that the law firm indexed for theclient.

There are at least two separate revenue streams that can result fromsearches originating from paper documents: one revenue stream from thesearch function, and another from the content delivery function. Thesearch function revenue can be generated from paid subscriptions fromthe scanner users, but can also be generated on a per-search charge. Thecontent delivery revenue can be shared with the content provider orcopyright holder (the service provider can take a percentage of the saleor a fixed fee, such as a micropayment, for each delivery), but also canbe generated by a “referral” model in which the system gets a fee orpercentage for every item that the subscriber orders from the onlinecatalog and that the system has delivered or contributed to, regardlessof whether the service provider intermediates the transaction. In someembodiments, the system service provider receives revenue for allpurchases that the subscriber made from the content provider, either forsome predetermined period of time or at any subsequent time when apurchase of an identified product is made.

18.2. Catalogs

Consumers may use the portable scanner to make purchases from papercatalogs. The subscriber scans information from the catalog thatidentifies the catalog. This information is text from the catalog, a barcode, or another identifier of the catalog. The subscriber scansinformation identifying the products that s/he wishes to purchase. Thecatalog mailing label may contain a customer identification number thatidentifies the customer to the catalog vendor. If so, the subscriber canalso scan this customer identification number. The system acts as anintermediary between the subscriber and the vendor to facilitate thecatalog purchase by providing the customer's selection and customeridentification number to the vendor.

18.3. Coupons

A consumer scans paper coupons and saves an electronic copy of thecoupon in the scanner, or in a remote device such as a computer, forlater retrieval and use. An advantage of electronic storage is that theconsumer is freed from the burden of carrying paper coupons. A furtheradvantage is that the electronic coupons may be retrieved from anylocation. In some embodiments, the system can track coupon expirationdates, alert the consumer about coupons that will expire soon, and/ordelete expired coupons from storage. An advantage for the issuer of thecoupons is the possibility of receiving more feedback about who is usingthe coupons and when and where they are captured and used.

19. General Applications

19.1. Forms

The system may be used to auto-populate an electronic document thatcorresponds to a paper form. A user scans in some text or a barcode thatuniquely identifies the paper form. The scanner communicates theidentity of the form and information identifying the user to a nearbycomputer. The nearby computer has an Internet connection. The nearbycomputer can access a first database of forms and a second databasehaving information about the user of the scanner (such as a serviceprovider's subscriber information database). The nearby computeraccesses an electronic version of the paper form from the first databaseand auto-populates the fields of the form from the user's informationobtained from the second database. The nearby computer then emails thecompleted form to the intended recipient. Alternatively, the computercould print the completed form on a nearby printer.

Rather than access an external database, in some embodiments, the systemhas a portable scanner that contains the user's information, such as inan identity module, SIM, or security card. The scanner providesinformation identifying the form to the nearby PC. The nearby PCaccesses the electronic form and queries the scanner for any necessaryinformation to fill out the form.

19.2. Business Cards

The system can be used to automatically populate electronic addressbooks or other contact lists from paper documents. For example, uponreceiving a new acquaintance's business card, a user can capture animage of the card with his/her cellular phone. The system will locate anelectronic copy of the card, which can be used to update the cellularphone's onboard address book with the new acquaintance's contactinformation. The electronic copy may contain more information about thenew acquaintance than can be squeezed onto a business card. Further, theonboard address book may also store a link to the electronic copy suchthat any changes to the electronic copy will be automatically updated inthe cell phone's address book. In this example, the business cardoptionally includes a symbol or text that indicates the existence of anelectronic copy. If no electronic copy exists, the cellular phone canuse OCR and knowledge of standard business card formats to fill out anentry in the address book for the new acquaintance. Symbols may also aidin the process of extracting information directly from the image. Forexample, a phone icon next to the phone number on the business card canbe recognized to determine the location of the phone number.

19.3. Proofreading/Editing

The system can enhance the proofreading and editing process. One way thesystem can enhance the editing process is by linking the editor'sinteractions with a paper document to its electronic counterpart. As aneditor reads a paper document and scans various parts of the document,the system will make the appropriate annotations or edits to anelectronic counterpart of the paper document. For example, if the editorscans a portion of text and makes the “new paragraph” control gesturewith the scanner, a computer in communication with the scanner wouldinsert a “new paragraph” break at the location of the scanned text inthe electronic copy of the document.

19.4. Voice Annotation

A user can make voice annotations to a document by scanning a portion oftext from the document and then making a voice recording that isassociated with the scanned text. In some embodiments, the scanner has amicrophone to record the user's verbal annotations. After the verbalannotations are recorded, the system identifies the document from whichthe text was scanned, locates the scanned text within the document, andattaches the voice annotation at that point. In some embodiments, thesystem converts the speech to text and attaches the annotation as atextual comment.

In some embodiments, the system keeps annotations separate from thedocument, with only a reference to the annotation kept with thedocument. The annotations then become an annotation markup layer to thedocument for a specific subscriber or group of users.

In some embodiments, for each capture and associated annotation, thesystem identifies the document, opens it using a software package,scrolls to the location of the scan and plays the voice annotation. Theuser can then interact with a document while referring to voiceannotations, suggested changes or other comments recorded either bythemselves or by somebody else.

19.5. Help In Text

The described system can be used to enhance paper documents withelectronic help menus. In some embodiments, a markup layer associatedwith a paper document contains help menu information for the document.For example, when a user scans text from a certain portion of thedocument, the system checks the markup associated with the document andpresents a help menu to the user. The help menu is presented on adisplay on the scanner or on an associated nearby display.

19.6. Use with Displays

In some situations, it is advantageous to be able to scan informationfrom a television, computer monitor, or other similar display. In someembodiments, the portable scanner is used to scan information fromcomputer monitors and televisions. In some embodiments, the portableoptical scanner has an illumination sensor that is optimized to workwith traditional cathode ray tube (CRT) display techniques such asrasterizing, screen blanking, etc.

A voice capture device which operates by capturing audio of the userreading text from a document will typically work regardless of whetherthat document is on paper, on a display, or on some other medium.

19.6.1. Public Kiosks and Dynamic Session IDs

One use of the direct scanning of displays is the association of devicesas described in Section 15.6. For example, in some embodiments, a publickiosk displays a dynamic session ID on its monitor. The kiosk isconnected to a communication network such as the Internet or a corporateintranet. The session ID changes periodically but at least every timethat the kiosk is used so that a new session ID is displayed to everyuser. To use the kiosk, the subscriber scans in the session ID displayedon the kiosk; by scanning the session ID, the user tells the system thathe wishes to temporarily associate the kiosk with his scanner for thedelivery of content resulting from scans of printed documents or fromthe kiosk screen itself. The scanner may communicate the Session ID andother information authenticating the scanner (such as a serial number,account number, or other identifying information) directly to thesystem. For example, the scanner can communicate directly (where“directly” means without passing the message through the kiosk) with thesystem by sending the session initiation message through the user's cellphone (which is paired with the user's scanner via Bluetooth™).Alternatively, the scanner can establish a wireless link with the kioskand use the kiosk's communication link by transferring the sessioninitiation information to the kiosk (perhaps via short range RF such asBluetooth™, etc.); in response, the kiosk sends the session initiationinformation to the system via its Internet connection.

The system can prevent others from using a device that is alreadyassociated with a scanner during the period (or session) in which thedevice is associated with the scanner. This feature is useful to preventothers from using a public kiosk before another person's session hasended. As an example of this concept related to use of a computer at anInternet café, the user scans a barcode on a monitor of a PC which s/hedesires to use; in response, the system sends a session ID to themonitor that it displays; the user initiates the session by scanning thesession ID from the monitor (or entering it via a keypad or touch screenor microphone on the portable scanner); and the system associates in itsdatabases the session ID with the serial number (or other identifierthat uniquely identifies the user's scanner) of his/her scanner soanother scanner cannot scan the session ID and use the monitor duringhis/her session. The scanner is in communication (through wireless linksuch as Bluetooth™, a hardwired link such as a docking station, etc.)with a PC associated with the monitor or is in direct (i.e., w/o goingthrough the PC) communication with the system via another means such asa cellular phone, etc.

Part IV—System Details

FIG. 4 is one embodiment of a system 400. Such a system may include atleast an input (e.g. a flatbed scanner) 410, logic (e.g. amicroprocessor) 420, storage (e.g. a hard-drive) 430 and a power source(e.g. a wall outlet) 440. In some embodiments, the system compriseslogic 420 and storage 430. In some embodiments, the system furthercomprises input 410 or a power source 440.

Input

In some embodiments, the system comprises an input. In some embodiments,an input may be used to create images of a document. As disclosedherein, in some embodiments, this input may comprise an optical sensorsuch as a CCD, a CMOS sensor, a linear array of optoelectronics or acombination thereof. In some embodiments, an image or optical sensor maycomprise means for inputting a printed document. In some embodiments,this input may further comprise an aperture. Such an aperture may beable to be shuttered either manually or automatically, such as a relaycontrolled by logic. Such an input may use optics. For example, theremay be a focusing lens across an aperture. In one embodiment, multiplelens may be employed such that a user may be able to focus to obtain amore desirable input. In one embodiment, reflective materials, such as amirror, may be employed to obtain a more desirable input. In oneembodiment, an input may be a separate device, such as a digital cameraor a scanner (including hand-held or flatbed). Such a device may be ableto communicate with other parts of a system through a wireless, wired,or other communications medium, including removable media.

Storage

In some embodiments, storage may be employed. Examples of uses include:storing variables; storing meta data; storing images; storing results;storing possible results; storing database information; storingsequences; matched groups; identifiers; segmented images; informationcorrelating identifiers and characters; language statistics; wordfrequencies; word combination frequencies; information about likelihoodof errors; or other information. Examples of storage include: Static,Dynamic, a multiple Data Rate (e.g Double or Quad), Rambus Direct,Random Access Memory (SRAM, DRAM, DDR-DRAM, QDR-DRAM, RDRAM,respectively), a Read Only Memory, Flash Memory, Secure DigitalInput/Output (SDIO), CompactFlash, a hard disk drive (including IDE andSCSI), Magnetic Memory (e.g. TMJ-RAM), FRAM, Compact Disc, Digital VideoDisc, tape storage, USB key, Storage Area Network, Redundant Array ofInexpensive Disks (RAID), Network Attached Storage; Zip drive;diskettes; or other computer accessible medium. In some embodiments,there may be multiple types of storage. For example, a system may have aRAID array of hard disk drives for possible results and SRAM for a highspeed cache. In some embodiments, storage may be contained in multipleplaces. For example, in one embodiment, possible results may be storedin a data center and images may be stored on a home computer. In someembodiments, storage may comprise a sequence of matched groups ofunrecognized images. A matched group may be comprised of unrecognizedimages that have been determined to represent the same charactersequence (possibly only one character). Such an identifier may beglobally unique; unique to this system; this embodiment of storage orthis addressable section of storage (e.g. one memory bank in a multi-wayserver). Such an identifier may be temporally unique. In someembodiments, an identifier may only be likely unique. In such anembodiment, error handling techniques may be employed as disclosedherein.

Power Source

In some embodiments, the system may comprise a power source that may beused to provide electricity to components of a scanner. In someembodiments, the system has multiple power sources for storing energy.One example is a battery that uses a chemical reaction to freeelectrons. These electrons then flow through connections to points witha lower electric potential. A gravitational analogue may be water at thetop of a hill, which may flow downhill towards points with lessgravitational potential. Another approach may be to have a power sourcethat is not wholly or primarily contained in this system. An examplewould be plugging one embodiment of this invention into a wall outlet.Another example would be plugging one embodiment of this invention intoa USB outlet such that a host device provides power to this embodimentvia this power source. In one embodiment, a fuel cell may be used togenerate electricity. One embodiment of a fuel cell consists of ananode, cathode, membrane and a catalyst. The cathode may have pathsetched in it filled with oxygen so that oxygen is distributed over thissurface of the cathode. The anode has pathways with hydrogen Thecatalyst begins a reaction between the hydrogen and oxygen. The membraneensures that electrons are not able to go directly across (i.e. betweenthe anode and cathode) and therefore must go through a circuit andprovide an electric current to a device. A power source may incorporateother parts to be able to provide power. Some embodiments may havecomponents that may be designed to receive Alternating Current (AC)whereas a power source component, such as a battery, may produce DirectCurrent (DC). An electrical device known as an inverter may be employedto accomplish this. In some embodiments, a power source may be intendedto deliver different voltages to different components. One way of doingthis might be to use resistors, or other current limiters forming avoltage divider. Another way of doing this might be use separateinverters such that each different voltage is produced by a differentinverter. A power source may also use fuses or other surge protectioncircuitry. In some embodiments, the system uses an uninterruptible powersupply.

Logic

In some embodiments, the system includes logic to associate a matchedgroup identifier with a character sequence. Examples of logic include:ASICs, Integrated Circuits (IC), FPGAs, Chipsets, Direct Memory Accesschips, microprocessors, reconfigurable logic, PLDs, CPLDs, softwareconfigurable ICs (including Adaptive Computing Machines from QuickSilverTechnology), System on a Chip (SoC), mixed signal SoC (includingSelf-Adaptive Silicon from Impinj), Digital Signal Processors, Storagewith lookup, quantum computing devices, array processors or vectorsprocessors. In some embodiments, combinations of logic may be employed.A character is a letter or other symbol that has been associated with aparticular member of a written language. A sequence comprisesinformation as to which characters are being referred to, and theirrelative placement. In some embodiments, this may be only one character.In such an embodiment, some sequence information may be implicit. Insome embodiments, this may be a string of at least two characters oridentifiers. In one embodiment, logic may associate a matched groupidentifier with a character sequence by querying a database. In such anembodiment, a microprocessor may retrieve from memory at least onematched group identifier such that it has retrieved an unrecognizedimage matched sequence. In an other embodiment at least two identifiersare retrieved. In an other embodiment identifiers for most of thematched groups are retrieved. Such an sequence may be used to query adatabase located in a data center accessible over the internet. Thisdatabase may return a character sequence. One way that this logic mayassociate the identifier with the sequence may be to replace allinstances of the identifier with the sequence. Another way to associatethese may be to store this information in a look up table. In anotherembodiment, these may be associated by placing the sequence data in alocation in storage determined by the location of the identifier (e.g.in the next memory address).

Capture a Scan

FIG. 5 is one embodiment of a character recognition method. This methodmay Capture a scan 510. Capture a scan may refer to creating a computeraccessible form of data from a document. A computer accessible form ofdata is information that may be read, modified or manipulated by logic.Examples include a Direct Memory Access chip interacting with RandomAccess Memory, a microprocessor retrieving data from a hard-drive, acellular phone receiving data over a wireless network or information onremovable media such as a USB flash drive. In one embodiment, capture ascan may comprise a printed document or a dynamically displayeddocument. A printed document is any textual content rendered on asurface or display that is not designed to be dynamic. Examples of aprinted document include: text on paper; engraved text; painted text;text created with ink; embossed text; or text created with a fusingprocess (e.g. iron-on transfers, laser printers). Further, text refersto the conveyance of text. So, if a piece of paper is dotted with inkeverywhere except space to spell out a word, this may be considered texton paper created by ink.

In one embodiment, a scan may be captured by an image sensor. Oneexample of an image sensor would be with a CMOS (computer chiptransistor technology) image sensor. Such a sensor may be able tophotograph a backlit display such as a computer monitor. Another examplewould be a Charge Coupled Device (CCD). Both of these are ways for acomputer chip to measure light as electrical signals across a grid ofsensors. In one embodiment, a scan may be captured by sensing light.Examples include: a linear array of light sensitive phototransistors; agrid of photocells; or a pattern of optoelectronic devices. In someembodiments, these devices may be placed such that they are able tobenefit from a light source reflected from a printed document. In oneembodiment, this may be a light bulb that is moved across a page.

The next step of this method is determine an unrecognized image matchedsequence 220. In this disclosure, determining takes a meaning closer tosetting direction than settling on a definitive answer. Determiningrefers to selecting an entity that is to be used for that process. Forexample, if an embodiment creates one sequence and proceeds to recognizethese characters and then creates a new sequence, it may have determinedan sequence twice, and may the first determination may have occurredbefore the characters were recognized.

An unrecognized image is an image, or a fraction of an image, that doesnot have a computer-legible form. Once an unrecognized image has acomputer-legible form, it may be a character or a sequence ofcharacters. In one embodiment, computer-legible may mean that the resultcomprises mostly correctly spelled words. In one embodiment,computer-legible may mean that 90% of the words are determined as theuser intended. To illustrate, a computer may have an image of the word“cat.” This image is computer accessible because there is data in a formthat a computer can manipulate. This image is not (yet) computer-legiblebecause a computing device has not discerned any text or characters inthis image. In this example, a computer may not have stored whether ornot characters are present. A user may input that there is text in thisimage, possibly by commanding the processor to execute OCR software. Acomputer may then segment this unrecognized image into smallerunrecognized images, one for each shape (i.e. ‘c’, ‘a’, ‘t’). Tocontinue with this example, the picture of an ‘a’ may constitute anunrecognized image. Later, once the OCR software has determined thatthis picture contains an ‘a’, this image is no longer a unrecognizedimage, but an image of a character. Therefore, the unrecognized image of‘a’ may not contain a character until an OCR process has determinedwhich letter this might be. Hence, if an OCR process determines thatthere is a 15% chance that a given image is a picture of a ‘c’, thisletter may be deemed a character. If this OCR process determines thatthis letter is actually an ‘e’, this image continues to have acharacter.

A matched sequence may comprise a matched group identifier andinformation about a sequential arrangement of these identifiers. Toillustrate, consider the word “determinative.” In one embodiment, acomputer may segment this unrecognized image into 13 unrecognizedimages: ‘d’, ‘e’, ‘t’, ‘e’, ‘r’, ‘m’, ‘i’, ‘n’, ‘a’, ‘i’, ‘v’, ‘e’. Atthis point, this data may be computer accessible, e.g. these images arein memory, but not computer-legible, i.e. the computing device has notrecognized any characters. In some cases, these images are segmentedbecause there was vertical whitespace between them. A matched sequencewould convey that the image of the ‘d’ appears before the image of the‘t’, and may further convey that the first image of the ‘t’ matches thesecond image of the ‘t’.

Determine a Matched Sequence

In some embodiments, a matched sequence of unrecognized images may bedetermined with a technique referred to as character offsets. Toillustrate, there may be a picture of a string of text in memory. Aprocessor may segment this image character by character. To do this, aprocessor may first determine which parts of an image constitute acharacter. These characters may then be compared to each other todetermine if a given character matches a previous instance of itself.

Figures may illustrate this process. FIG. 6 is one embodiment ofdetermining a matched sequence of unrecognized images. The first step610 ends with the scanned image in storage. To accomplish this, aprocessor may trigger an image sensor. This processor may respond to anacknowledgment of this sensor by issuing commands to move image datafrom this sensor to memory. In the next step 620, the processor mayanalyze this image data. It may use techniques from art relating tooptical character recognition such as white space analysis orcharacterization, vector or blob analysis, a width based approach orother techniques to determine which parts of an image form a character.A processor may then segment this image into these parts. The followingstep 630 is where the processor loads the first segmented unrecognizedimage from memory. Next, 1240 the processor will compare this characterto characters in the cache. In one embodiment, this may be done bysubtracting the two images from each other. If the resulting pattern isbeneath a total size threshold they may be determined to match. Inanother embodiment, this may be done with feature extraction. Here, animage is decomposed into vectors. If these vectors are similar enough inlength and direction, these characters may be deemed a match. Being thefirst character in this example, the cache will be empty. Therefore,this iteration proceeds to 650 where this character is added to thecache. The processor may write this representation to memory, and maystore other data enabling it to find this data again. This may beimplemented as a linked list or as an array (with a variable signifyinglength). After this, logic may output this identifier 660 to two places.A first step in one embodiment of this process is this identifier willbe added to the output stream. Because this is the first character, theoutput is currently of zero length. This identifier will also be storedwith the character in cache so that if this character is found again, itcan be identified. Continuing with this example, 680 the processor loadsfrom memory data to determine if there are more characters. Assumingthere are, this process returns to 630. Similar to the last character,it is loaded 630, and then compared to the cache 640. To illustrate,let's assume that this character matches the first. This processcontinues to a new state 670. In this state, the processor retrievesfrom memory the identifier of the matched character. This identifier isthen added to the output stream. Because this character matches onealready in the cache, the cache is not updated nor is a new identifiercreated. This iteration continues to 680. If the processor determinesthat there are no more characters to consider, this routine finishes690.

FIG. 7 illustrates one embodiment of this process for the word“determinative.” Each line (containing a letter, parentheses, and anumber) is representative of an iteration. The color red is used toindicate new data in this iteration. On the left, a red characterdenotes which character has been read in. In the middle column is arepresentation of the cache. Inside the parentheses are the uniquecharacters that have been previously seen. In the first row, for theletter d, this is empty. The column on the right is a running record ofthe output stream. In this embodiment, the red numbers are being addedon the right because each new character is further to the right than theprevious characters. Sometimes this number is larger than any previousone. This is because it represents a new character and in thisembodiment identifiers are produced in numeric ascending order.Sometimes a number is repeated. This means that this character initiallyappeared at the location held by the earlier identifier. In thisexample, the letter ‘e’ appears three times, but all share theidentifier ‘2’ because the first ‘e’ was assigned a ‘2’. The identifiershave been described as numerals, but this is not a requirement. Further,these identifiers do not need to be in an order. In some embodiments, aprocessor may be used to check that a identifier is unique.

FIG. 8 is one embodiment of another approach to determine a matchedsequence of unrecognized images. To illustrate, this embodiment may beenvisioned as sliding an image of text across itself. This embodimentbegins 810 after a sensor image has captured an image. A processor inthis embodiment may create a queue of pixels to compare with thisoriginal image. This queue may be vertical slices of a copy of thisoriginal image. Another approach may be to use address pointers to keeptrack of which vertical slices are to be compared, and then createtemporary copies of these slices in the processor. The following step820 compares one length. Length refers to a horizontal width of avertical slice. This may be one pixel, or multiple pixels. It may beheuristically determined based on whitespace. This slice may be theentire image. This slice is compared to slices from an original image.This may be done by comparing this slice sequentially to slices from anoriginal. In one embodiment, this comparison slice is sequentiallycompared to a corresponding slice one step over. A step may be the samedistance as a horizontal width. A step may be a pixel or multiplepixels. In the following stage of this process 830, this processor mayrecord to memory where this image matches itself. This data may containwhich vertical slices match other vertical slices of this originalimage. A matching section may or may not be a character (e.g. it mightbe two characters that only appear in order). The next step 840 is todetermine if this comparison is complete. Comparison does notnecessarily refer to one slice, but to this larger process. One way todetermine if this comparison is complete if there are no more slices tocompare.

FIG. 9 is an illustration of one way to imagine one embodiment of thisprocess. This shows a step by step breakdown of using a single slice tofind character offsets. The exemplary steps are numbered, such as the‘1’ 900 shown. Lines 910 are used to separate the steps. An image of theword determinative is being compared. On the left is a slice 920 and onthe right is a copy 930 in storage. When an overlap is found, thisillustration designates this with a triangle 940. FIG. 10 is anotherillustration. Here, the slice copy 1020 is shown above the copy inmemory 1030 so that it may be clearer why a match is found 1040.

FIG. 11 illustrates one embodiment of how this recording may occur. Inthis embodiment, one difficulty may be in determining which parts of anunrecognized image are a character. One approach may be to subdividethis image into sections that have a whole number of characters. In oneembodiment, this process may be iteratively completed as matches arefound, or may be started after all matches have been found. In 1110 thisimage is one segment, i.e. an image of a whole number of characters(this image may be cropped to the section enclosed in whitespace). Thenext section 1120 sees if there are more matching subsections toprocess. If there are no more, then this process may terminate 1170, butif there are then this iteration proceeds to the following step 1130. Inthis section 1130 these sections are recorded. One dimensional locationmeasurements may be sent to memory. One way to associate these segmentswith their matching counterparts is to use an identifier. Anotherapproach is to store them in memory such that their relative locationsprovide information as to how they match (e.g. each matching pair isstored sequentially, and odd numbered matches have one repeated regionsuch that there is an even number). The next step 1140 determines if anyof these matching segments overlap with any segment. This may be whereone segment entirely encompasses another, or where only a section ofeach overlaps. In the following step 1150 these segments may besubdivided. This may occur where a first segment has multiple charactersand a second segment has smaller number of these characters. Forexample, a first matched segment may contain “ing” and a second segmentmay contain “in.” This process may then subdivide these into segmentscontaining “in” (i.e. what was matched) and “g” (i.e. what was left). Ifall segments begin with a whole number of characters, removing a wholenumber of characters will also leave a whole number of characters. Inthe next step, 1160 each of these segments may be stored as the largestsegment that is either completely overlapped or entirely free ofoverlap. This process may be similar to when locations are stored 1130.In one embodiment, the same system of correlating matching segments isused. After this process, an original image will have a number ofmatching segments identified. Space between these segments (or betweenthese segments and at least one edge of this image) may then be treatedas new segments that do not match any other segment. It may be that eachcharacter has its own segment. In one embodiment, character analysistechniques, such as blob analysis or connectedness analysis may be usedto further subdivide segments. These segments may then be used todetermine the text on which they are based. In one embodiment, thesesegments are represented as offsets or identifiers, and these are usedto look up which text would produce these offsets or identifiers. Insome embodiments, the storage that contains this information may bepopulated with data that can account for segments containing multiplecharacters.

Determine a Computer-Legible Result

The next step is to determine a computer-legible result 530. Oneembodiment of a process to accomplish this is illustrated in FIG. 12.This process may begin after a matched sequence of unrecognized imageshas been determined, and logic has stored this result 1210. In oneembodiment, this result may be a series of identifiers. For example,determinative may be 1232456783692. In another embodiment, this sequencemay be similar to run length compression. In this example, determinativemay be 0279004000000. In such an embodiment, a matched group may beidentified by a location in memory or other storage address. In anotherembodiment, an sequence may indicate where this character last appeared,so determinative may be 0002000007409. Continuing with this figure, thisiteration may then be in a stage where the matched sequence has beensegmented 1220. Some embodiments may omit this step and proceed directlyto getting results 1230. Such an embodiment may simply treat whitespaceas a character. In one embodiment, whitespace may be used to segmentthis matched sequence. This may allow some embodiments to perform thenext step 1230 in parallel or to use less storage. In one embodiment,whitespace may need to have a minimum length, such that the logic isprogrammed to detect that this a break between words. In one embodiment,identifiers may be relabeled such that this segment only refers tounrecognized images in it. In one embodiment, an original matchedsequence may be saved and associated with this new set of identifiers.

The next step 1230 may create a result from this matched sequence. Inone embodiment, this result may be in ASCII, Unicode, or EBCDIC format.In one embodiment, a sequence may be used to address into a database.Each address in this database may contain character strings, words,acronyms, proper nouns, phrases or other possible results. In oneembodiment, possible results may comprise fractions of words. In oneembodiment, a database may be able to combine possible results intolonger possible results. For example, the first three characters of amatched sequence segment may match cat and Catherine. As moreidentifiers are considered, possible matches may include “cat hair” andCatherine. In one embodiment, segmentation 1220 may be performed on asubsegment. In such an embodiment, this segmentation may be performed onthe identifiers that refer to characters beyond one possible result(e.g. the character after the ‘t’ in cat). In one embodiment, longersegments (including complete sequences) may be queried before shortersegments. In one embodiment, segments may be queued according tofrequency of identifiers (e.g. segments with a disproportionate numberof repeated unrecognized images of a dearth of repeated unrecognizedimages may be queried first). In some embodiments, all results may besaved to storage as part of this step. In some embodiments this may betemporary 1270.

In some embodiments, determining a computer-legible result may furthercomprise error handling. In one embodiment, error handling may compriseerror correction, detection, prevention or a combination thereof. In oneembodiment, possible results may be stored such that errors may beprevented. For example, in some cases the letter ‘m’ may be similar tothe sequence ‘rn’. Such an embodiment may store ‘rn’ along withpossibilities for ‘m’. One example may be returning both “mom” and“morn” as possible results, because some input devices may not be ableto distinguish between these two.

In some embodiments, possible results may be found by treating at leastsome of the matched sequence as unknown. For example, some parts of apossible results query may be replaced with an identifier signifyingthat this segment of the query is unknown, e.g. a wildcard character.Such a query may return more possible results. In some embodiments, thisquery segment may be of a varying length. For example, an image may beof such poor quality that it cannot be matched to anything. In such anembodiment, logic may not be able to determine how many matched groupidentifiers ought to have been generated. In one embodiment, logic maydetermine that an unrecognized image is likely to be a capital letteramong many lower case letters. In one embodiment, the identifier forsuch an image may be treated as an unknown. For example, if the inputtedtext is “Determinative results are common,” one embodiment may create aquery that removes the ‘D’ and used matched group identifiers for theother unrecognized images. In one embodiment, storage may comprise onlyone character sequence that ends in “eterminative.” Such an embodimentmay be able to return “Determinative.”

In some embodiments, determined results may be used to determine otherresults. For example, in one embodiment, if a segment returns one uniqueresult, then the matched group identifiers may be associated with thecharacter sequences. If such an identifier appears elsewhere, such as inthe next segment, such an embodiment may be able to replace theidentifier with the character sequence. In one embodiment this maycomprise a look aside table. In one embodiment, logic may be able tostore a flag, or other indication, that all matched group identifiershave been associated with character sequences.

In some embodiments, possible results may be added. In variousembodiments this may be done through data entry or interfacing withvarious embodiments of storage. In one embodiment this may be doneautomatically. For example, a given segment of a matched sequence maynot return any possible results (e.g. a trademarked word that thissystem has not stored). Other segments may be uniquely determined, andtherefore these identifiers may be associated with character sequences.In such an embodiment, this character sequence may be added as apossible result to storage. In one embodiment, certain error handlingtechniques may be applied before this word is added. One example maythat a system must produce this possible result multiple times before itis stored. In another embodiment, traditional OCR techniques may beapplied to determine characters for these segments. In one embodiment,traditional OCR techniques may be applied to detect errors. One examplemay be doing “spot checks.” Another example may be confirming rareresults, or other results that a system may deem error prone. These OCRtechniques may be further employed to correct errors.

This iteration may then decide if this result is unique 1240. If one andonly one result is returned from the previous step 1230 then this resultmay be considered unique and this process may proceed 1260. If multipleresults are returned then this process may proceed to apply constraints1250.

In one embodiment, apply constraints 1250 may entail using constraintsacross multiple segments. To illustrate, consider the phrase “officescanner.” In one embodiment, this string may be segmented into words. Insuch an embodiment, the identifier string for “office” may match“apples,” “office,” “otters” and “udders.” Similarly, the identifierstring for “scanner” may match “chatted,” “scanner,” “shipped” and“tripped.” However, if constraints are considered for “office scanner”(e.g. the second to last letter of office, ‘c’, must match the secondletter of scanner) fewer results are possible. Of these 16 originalcombinations (four possibilities for each word), only “office scanner”and “tripped udders” satisfy the requirement imposed by the character‘c’. If a larger dictionary or database is used to generate results thenmore segments or more constraints may be considered. In one embodiment,information about a language may be stored to assist with this processof constraints. In one embodiment, possible results may each beassociated with a usage frequency, either general usage, usage by thisuser or a combination. In one embodiment, such frequency information mayexist for strings of possible results. For example, “and” and “but” maybe common words in English, but may be unlikely to follow one another.In one embodiment, constraints may be applied in combination. In oneembodiment, some constraints may be conditional.

In one embodiment, this system may comprise some error handlingcapabilities. For example, imagine an unrecognized sequence that hasbeen segmented into ten parts. In this example, for simplicity, eachsegment is a word. In one embodiment, a system may try to handle onemistaken segment. It may compute the probability, based on wordfrequency, of each combination of the nine other words. To illustrate,imagine that these ten words have the following likelihoods of appearingin a ten word phrase: 50%, 32%, 26%, 41%, 0.01%, 17%, 38%, 24%, 44% and37%. Nine of these combinations will have a word with a 0.01%probability of occurring, and this exemplary may determine that thisword may be due to an error. In some embodiments, error handlingtechniques disclosed herein may be used.

At this point, our exemplary process may determine if there are moresegments 960. In some cases this may be part of an effort to generatemore constraints. In some cases, a result is unique, so more segmentsmay be processed. In one embodiment, this step may be performed bychecking a variable in memory, or a location pointed to. If there aremore segments, this process may return to process further results 1230.

If there are no more segments this process may proceed to store results1270. In one embodiment, unique and non-unique results may be stored(this includes one or zero results). With such an embodiment, a user maybe presented with an indication that more than one result exists. Withsuch an embodiment, a user may be able to select a desired result. Inone embodiment, some data may be removed from storage, or marked fordeletion. Such data may include eliminated possible results, or interimdata. In one embodiment, conditional constraints may be used to force aunique result. For example, in one embodiment, a segment may refer toeither “south” or “couth.” Such an embodiment may return “south” as aunique result because it is more common in usage than “couth.” In oneembodiment, these results may be stored in ASCII, Unicode, or EBCDIC. Inone embodiment, these results may be in a standalone file. In oneembodiment, these results may be stored in a file with other results. Insome embodiments, these files may comprise text, databases, compresseddata, encrypted data (e.g. blowfish, DES, rijndael, elliptic curve, RSA,IDEA, RC5 or ElGamal), or statistics. In some embodiments, these resultsmay be stored in storage. Examples include memory, a hard-disk drive andremovable media. In one embodiment, Store results may refer tocommunicating results to a user. This may be because storage is used inpreparing the communication. This may be because the user's memoryfunctions as storage.

From the foregoing, it will be appreciated that specific embodiments ofthe system have been described herein for purposes of illustration, butthat various modifications may be made without deviating from the spiritand scope of the invention. Accordingly, the invention is not limitedexcept as by the appended claims.

1. A method in a computing system for character recognition, comprising:capturing a scan; determining a matched sequence of unrecognized images;and determining a computer-legible result.
 2. The method of claim 1wherein the determining is performed without using language statistics.3. The method of claim 1 wherein the determining is performed withoutstoring information about distances between unrecognized images.
 4. Themethod of claim 1 further comprising querying a database.
 5. The methodof claim 1 wherein the determining is performed without logic withcharacter statistics.
 6. The method of claim 1 wherein the determiningis performed without logic with language statistics.
 7. The method ofclaim 1 wherein the computer-legible result is usually error-free. 8.The method of claim 1 wherein the computer-legible result comprisesmostly words.
 9. The method of claim 1 wherein a computer-legible resultfor at least 95% of documents has an error rate smaller than current OCRtechniques.
 10. The method of claim 1 wherein a computer-legible resultfor at least 95% of documents has an error rate smaller than 10%. 11.The method of claim 1 wherein a computer-legible result for at least 95%of documents is more accurate than a probabilistic method.
 12. Themethod of claim 1 wherein the capturing is performed without storing animage of an entire document.
 13. The method of claim 1 wherein thecaptured scan comprises an image of less than a full document.
 14. Themethod of claim 1 wherein the captured scan is of less than 20 words.15. The method of claim 1 wherein the computer-legible result does notcomprise any unknown characters.
 16. The method of claim 1 wherein thecomputer-legible result does not comprise any probabilistic information.17. The method of claim 1 wherein the computer-legible result consistsof characters or character representations.
 18. The method of claim 1wherein the determining is performed without a substitution cipher. 19.The method of claim 1 wherein the matched sequence does not consist ofintegers.
 20. The method of claim 1 wherein at least one unrecognizedimage does not consist of one character.
 21. The method of claim 1wherein at least one unrecognized image comprises at least twocharacters.
 22. The method of claim 1 further comprising accessingstorage comprising at least 10 sequences of at least 4 characters. 23.The method of claim 1 wherein the matched sequence comprises 5identifiers.
 24. The method of claim 1 wherein the matched sequencecomprises offsets.
 25. A system, comprising: storage containing amatched sequence of unrecognized images; and logic to associate amatched group identifier with a character sequence.
 26. The system ofclaim 25, further comprising a power source.
 27. The system of claim 25,further comprising an input comprising an optical sensor.
 28. The systemof claim 25 wherein at least one character sequence does not consist ofone character.
 29. The system of claim 25 wherein the storage furthercomprises possible results.
 30. The system of claim 25 wherein thestorage further comprises at least 10 sequences of at least 4characters.
 31. The system of claim 25 wherein the logic does notperform a substitution cipher.
 32. The system of claim 25 wherein thestorage does not comprise single character frequency information. 33.The system of claim 25 wherein the storage does not comprise two orthree character sequence frequency information.
 34. A system,comprising: a means for inputing a document; a means for determining amatched sequence of unrecognized images; and a means for determining acomputer-legible result.
 35. The system of claim 34 wherein the documentis a printed document.